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万字长文,关于概率要素和统计学要素在游戏设计中的运用,下篇

发布时间:2015-08-14 09:52:53 Tags:,,

篇目1,阐述随机性在电子游戏中的使用

1975年秋天, Reginald‘Rusty’Rutherford看到了一只属于他的怪物在计算机屏幕上徘徊着。监视器显示上橙色的光呈现出了一张地图,一个佩带着宝剑的小小英雄,以及《龙与地下城》那般庄严的背景信息。这便是《Pedit5》,最早出现在计算机上的角色扮演游戏。

Rutherford是致力于伊利诺伊大学自动教学用程控逻辑(PLATO)的一名程序员。出现于60年代的硬件与最初的网络计算机系统一起被当成教育工具投入使用。在70年代,也就是英特网诞生前10年,PLATO能够与世界各地150多个位置相连接,并且它也不断扩展向新地区。系统中的空间是有限的,但是Rutherford的团队已经可以利用2个未被使用过的文件,即标记为“Pedit4”和“Pedit5”。

不理会相关规则,Rutherford采用了Pedit5,并开始基于《龙与地下城》而开发自己的游戏。同时Pedit4也成为了他的新游戏的使用手册。Rutherford尝试着去模仿一款丰富且复杂的桌面游戏,但是他的编程却缺少复杂性。每个地牢都包含一个楼层和50个房间。创造者所面对的难题是如何确保游戏体验不会变得乏味。

他在《龙与地下城》的规则手册中找到了解决方法:随机性。如果Rutherford让PLATO能够自行决定怪物和财宝的位置,那么潜在关卡布局的数量将飙升,游戏将能更有效地吸引玩家的注意。不久之后他甚至抛弃了PLATO系统这份工作而专注于游戏的创造中,并且这两个秘密文件在后来也变成了整个电子游戏类型的基础。

将随机性整合到《Pedit5》的设计中引起了一个有关计算机时代的古老问题:当我们放手,让一切听天由命时会怎样?但这也是游戏在各个世纪会以不同形式呈现出来的问题,并伴随着将自己的命运投掷在随机机制中的人类。20世纪基于骰子的桌面游戏,如Ludo和印度双骰游戏便可以追溯到1914年由德国人设计的《Mensch ?rgere Dich Nicht》。我们可以将其粗略地翻译为“喂,别生气”,即意味着游戏带有让人沮丧的随机性关卡。一款1965年的家庭类游戏《Trouble or Frustration!》也是完全依赖于运气元素。在这款游戏中,玩家必须滚动骰子并获得点数6才能出发。这些游戏都是受到印度双骰游戏所谓的“交叉和圆圈”家庭规则的影响。

Pachisi(from edge-online)

Pachisi(from edge-online)

英国建筑师兼作家Edward Falkener在其1892年出版的书籍《Games Ancient And Oriental And How To Play Them》中探索了16世纪的印度双骰游戏,根据报道,印度皇室会将活人当成棋子,并在一个巨大的棋盘上操纵着他们玩游戏。Falkener写道:“印度阿克巴皇帝和他的侍臣们会一起玩这种游戏;16个年轻的奴隶被命令穿上代表不同棋子的颜色的衣服,并根据骰子的滚动朝方形移动。”根据Falkener,玩家是用来自宝贝螺(一种海产软体动物)中的6个贝壳代表骰子。然后玩家会计算有多少贝壳是开放一面朝上。所以印度双骰游戏是最早基于随机掷骰子原理的游戏(人类是在20世纪20年代一个苏美尔人的坟墓中发现这一事实)—-这要追溯到公园前2600年,那时候他们还是使用四面体的骰子。

可以说所有的这些游戏都是公元前人们最喜欢的消遣方式。它们都属于机会游戏——围绕着简单的运气工具而创造的简单系统。比起单纯的技巧,随机性更像是上帝判定竞争结果所使用的最简单的方法。

尽管今天的游戏(不管是桌面游戏还是数字游戏)得益于早前的随机工具,但是直到近十年我们才真正开始发现随机性的闪光点。

《我的世界》中有一个“创造新世界”的矩形按键。点击它,片刻之后你便会发现自己在一个爬满树藤的树干前。或者你站在没膝的水里。也许你将出现在一个小南瓜地,附近还潜伏着一只斑点猫。固定的算法结合随机性或玩家特定的“播种”顺序便能创造出各种可能的场景。因为每个种子都会长成一个不同的世界,所以很少按键能够带来《我的世界》中那样的冲击感。

Minecraft(from edge-online)

Minecraft(from edge-online)

《我的世界》是关于在一个专属于自己的世界中机智地创造,生活和工作。结果是,没有GameFAQs能够告诉玩家在任何特殊洞穴前面会出现什么,YouTube上的教程也未告诉玩家在哪里可以找到钻石矿。因为互联网的出现,电子游戏已经不能够向玩家隐藏秘密了。所以即使带有随机性,《我的世界》也是可以获得解决。你可以花几分钟时间在一个基本关卡中学习游戏是如何运行的,但是如果你想继续生存下去并不断发展,你就需要精通游戏。

与桌面游戏一样,带有许多随机性设计的电子游戏从传统意义上来看也是抽象的。如带有“T”和“L”形状以及更难懂的长形砖块的《俄罗斯方块》。或者基于匹配同样颜色对象这一游戏理念的《宝石迷阵闪电战》。但是这两款游戏却都不具有真正的随机性。举个例子来说吧,《俄罗斯方块》会随机洗牌,但却会分散所有砖块类型去保证玩家不会连续遇到无数个“Z”砖块。

但是游戏却不需要为了从大量取决于机会的游戏体验中受益而趋于抽象化。《暗黑破坏神》便是利用其自身的内容(游戏邦注:包括掉落的战利品和地图布局等)逐渐朝着随机性发展。2009年发行的《边境之地》(带有传说中1775万的枪支)也使用了一种随机道具生成系统去支撑其市场营销活动。甚至大受欢迎的大众市场系列游戏,如《战争机器》也利用随机性去缓解重复机制。在《战争机器:审判》中的死亡和重新开始及其Smart Spawn系统都会创造出一组全新且随机挑选的敌人与你战斗。

Spelunky(from edge-online)

Spelunky(from edge-online)

半授权随机性是《洞穴探险》的核心,Derek Yu将平台元素和roguelike游戏类型有效地整合在一起。与之前的许多游戏一样,《洞穴探险》也突出了随机生成关卡,即利用来自一个固定组合的随机砖块去构建房子。然后等式便会运算一组检查,并生成带有怪物和阻碍的关卡,这同样也是基于随机性,但也遵从了智能规则而获得了平衡。你只有一次机会能够面对这种设置。而最终结果便是,要么你被杀死而重新开始,要么你获得成功而朝下个世界(由一个全新砖块和公式创建而成)前进。

关于这种方法的最显著的利益便是,游戏可以避免不断重复。比起进入一个你曾经攻克的世界,《洞穴探险》的关卡会让你去克服一个由同样法律所锻造的不同世界。游戏希望你使用从之前关卡中学到的知识去探索全新领域。

随机性能够不断带给玩家(甚至是创造者)各种惊喜。但是游戏设计中的随机性都是关于世界和武器吗?我们是否值得去创造带有无尽内容的游戏?Yu并不是这么认为的。

他说道,使用随机性去拉长游戏长度是“最糟糕的情况,除非你仍然有新内容能够带给玩家并让他们获得有趣的体验。”对于Yu来说,这意味着不断地观察并学习新事物,接受挑战,并“扩展有关世界的理念。”

关于无趣地执行随机系统的一个典例便是老虎机。拉杆并观看着樱桃和柠檬旋转着并不能教会你任何内容—-旋转只会带来无意义的噪音。尝试着去理解并控制它就像是预言电视静态模式一样,但是这却不能阻止人们沉浸于这一机器中,因为它能够创造出刺激感。Yu将这种情况称为“随机性的内在瘾性”。

人类大脑总是能够发现噪音中的模式,所以当随机性的执行是静态时,玩家便会理解为这是来自隐藏的控制器。

让我们着眼于Valve在2007年的大作《求生之路》,虽然这款游戏拥有固定的关卡,但却在其它设计领域中使用了随机性。每次游戏时我们都能够在不同位置发现不同数量的重要资源,如健康包和武器等。游戏也随机设置了僵尸群,有时候会涌现出大量的僵尸—-而这一切都是受到游戏AI系统的控制,即The Director。

The Director只是关于支配《求生之路》中随机元素的算法集合的名字。它不带有任何感情或目标,但是玩家必须将The Director的想法当成一个实体,并如此进行讨论与思考。

在YouTube上名为《The Director Hates Us》的视频中,一名玩家描述了其最近的游戏体验。他说道,The Director会选择性地攻击自己与好友,并隐藏重要的健康包,直到他们来到关卡的最后环节。他在视频的描述中写道:“他会在采访车和急射机枪右边设置一个女巫。”

当然了,幕中人只是机器,但是因为它所递交的裁定和结果都是不可预知的,所以玩家才将其误以为是人类。The Director(或者编写出它的代码)是因为不可预测性才变得如此像人类。

基本上人类总是希望能够看到代理和模式,即使现实中什么都没有。我们很容易想象到特性是如何在人类发展过程中保留下来。游戏《100 Rogues》的设计师,同时也是《Game Design Theory: A New Philosophy For Understanding Games》的作者Keith Burgun认为,当人们在玩游戏时,这种继承性便会表现出来。他说道,当孩子们在玩《Candyland》这样的桌面游戏时,他们便会相信在自己的骰子角色背后拥有一个代理。甚至当人们逐渐长大并开始清楚自己不能真正控制骰子这样的事物时,他们仍会将在大多数随机游戏中的成功归功于自己。

Burgun说道:“人们会因为在《龙与地下城》中摇出一个20点而站起来开心庆祝。他们允许自己参与这种人类可以做的事,但是他们却清楚其实并不存在任何代理。”Burgun沉默了一会,用自己一贯的方式和语气说道:“这同样也是上帝所赐予我们的。”

Keith-Burgun(from edge-online)

Keith-Burgun(from edge-online)

在Rusty Rutherford离开了原本的工作和自己所创造的先驱游戏后,18岁的Paul Resch来到了伊利诺伊大学的地下室。Resch沿着混凝土阶梯走下,走进一间泛着橙色灯光的房间,这是PLATO系统所发出的光线。一群学生集中在机器附近,正在使用这些机器去学习果蝇的杂交(生物课)。看来这是一项有关教育游戏的项目。

他自己的班级还未上课,但是Resch却非常想要试试这些先进的机器,所以他便假装自己也是一名生物学学生,偷偷地访问了一个终端。正是在那里,他发现了一个被遗弃的文件:Pedit5。Resch联系了一些好友,并在接下来几个月的时间里开始修改代码去完善它。Resch创造了基于网络的多人聊天系统,并将其整合到游戏中。他还添加了来自《龙与地下城》世界中的更多规则,他们甚至还想办法获得了Tactical Studies Rules(游戏邦注:TSR,《龙与地下城》在那时候的发行商)的使用许可。

TSR的反应其实也很迷惑。Resch解释道:“他们回应道,‘虽然我们不清楚你们在说什么,但是没关系。’”在做出了更多改变后,Resch认为他的游戏已经取得了很大的完善,足以成为一款新游戏了。他将这款被修改过的游戏命名为《Orthanc》,即以《指环王》世界中Saurman的高塔命名。

最后,Resch设计了一套算法能够自动为《Orthanc》创造出随机关卡。每隔六个月,该算法便会运行《Orthanc》早前的世界,并用一些全新,但也是临时的世界去取代它们。在关卡改变出现前几周,PLATO用户便能够获得相关信息:“新关卡即将出现。”这是一种友善的提示,但同时也是一种警报—-如果用户在关卡诞生时仍继续游戏,那么他们所面临的整个世界便会蒸发掉,而一个全新的世界便会突然出现。从而导致他们可能会被困在四面都是墙壁的空间里。

如今已经55岁的Resch曾在雅达利,苹果和谷歌等大型公司中工作着,并且在70年代中期他创造出了谁也不曾想到能与电子游戏勾搭在一起的功能。

但是他为什么设计出一个只能一年执行2次的复杂算法?为什么不直接设计关卡本身?Resch的答案既让人信服又让人畏惧:他知道自己不可能永远创造出新内容。所以在某种程度上他算是采用了一种保险政策,即为自己不可避免的死亡提早做好准备。

游戏中的随机性通常都是关于替换或模拟人类。而Rutherford和Resch都通过对于游戏的完善使之成为一种自我补充的对象。就像在《战争机器:审判》中的Smart Spawn系统便算是游戏中的一个小小设计师,它观看着玩家如何游戏,并提供给那些重复某一序列的玩家全新体验内容。

像《洞穴探险》和《我的世界》等电子游戏也利用了随机系统,但是却不是为了延长游戏长度,而是为了在游戏中体现出让人惊讶的场景。这种随机性的使用与Ludo或印度双骰游戏中的掷骰子且纯粹基于运气的获胜是完全不同的。

当游戏中出现了一些设计师不认为是电子游戏所能够给予的内容时,这些惊讶感会会迸发出来。所以当我们在选择创造或体验随机游戏时,最好能够问自己一个简单的问题:我们是否想要收到惊喜,或者我们只是想要感受到运气?

篇目2,关于游戏设计中的机会和技能

作者:Lennart Nacke

欢迎来到本课程的第五堂课:关于游戏设计的基本介绍。在阅读本文前请确保你先了解教学大纲和课程信息。今天我们将讨论游戏设计中的机会和技能。本文是遵循我们的教科书《Challenges for Game Designers》的第五章和第八章。我的灵感同时还源自Schell的《The Art of Game Design》(第十章内容)以及Adams和Rollings 的《Fundamentals of Game Design》(第十一章内容)。

突出有意义决策的游戏并不总是要求或会唤醒玩家的技能。有些游戏是完全依靠机会。比起技能更多地依赖于机会的游戏通常是关于儿童游戏或博彩游戏。为什么这一区别这么重要?玩家将一直玩玩玩玩玩。不要太快摆脱机会的概念。机会游戏可以非常吸引人,因为它们让带有不同技能的玩家可以基于公平的竞争率而玩游戏。这样的游戏是面向所有人;是面向那些习惯于掷筛子游戏玩法以及那些喜欢感受敌人眼睛所迸射出的恐惧感的人。有些人甚至认为失败以及伪装很有趣。运气游戏似乎强调了更加容易达到的目标。

另一方面,像一字棋这样的游戏则是纯粹基于技能的游戏,并且一旦玩家想出了主要策略便能够精通这类游戏。

关于我将要说的内容似乎看起来很疯狂,但对于游戏将机会作为一种游戏机制的确存在一些理由:

游戏设计师想要阻止或延迟玩家解决问题。

游戏设计师希望游戏玩法达到平衡并且对于所有不同类型的玩家来说都具有竞争性。

机会将提升你的游戏系统的元素多样性。

机会能够在你的游戏中创造出戏剧性的时刻。

机会能够增强你的游戏的决策性。

游戏平衡

Adams和Rollings将一款平衡的游戏描述为“对玩家来说够公平,不管是太难还是太简单,让玩家技能成为决定胜负的关键元素。”平衡的游戏将具有如下特征:

游戏提供了有意义的选择。一些策略能够帮助玩家获胜。游戏中不存在主要的获胜策略。

机会的角色不如技能重要。带有更厉害技能的玩家比拥有糟糕技能的玩家更有可能获胜。

游戏的难度级别是一致的。玩家会感觉到游戏挑战一点都不唐突,并且是在他们合理的能力范围内。

在玩家对抗玩家的游戏中同样也会出现如下特征:

玩家会认为游戏是公平的。

任何在游戏初期落后的玩家都有可能在游戏结束前获得反超的机会。

游戏不可能出现玩家带有不公平能力的情况。

基于运气和技能平衡的游戏测试

当平衡游戏时,需要考虑的一个重要元素便是游戏中技能和运气元素的平衡。以下是标志着你的游戏缺少技能/运气平衡的情况:

你的玩家感到无聊。这标志着游戏失去了有趣的决策而只剩下过多的运气元素。

你的玩家只会在未轮到自己的时候感到无聊。这说明你的游戏可能缺少一些策略元素,即玩家在自己的回合所做的任何事都不会影响到其他玩家的回合。

你的玩家并不会沉浸于游戏中,并且会对自己要做什么感到困惑。这标志着游戏中存在太多决策或者太多信息需要玩家去接收。

其中一个玩家绝对性地压制住所有其他玩家。这预示着你的游戏太过侧重技能,而其中一个玩家便精通了这一技能。为了确保游戏对于带有不同技能水平的玩家来说是公平的,你就需要在此添加一些运气元素。

通常情况下,添加“运气”到游戏中也就是等于添加随机元素。在桌面游戏中,这通常是由掷筛子或洗牌所决定。如果你发现使用了太多这些随机元素,你便可以使用一些自动前进取代它们(例如在一个回合期间多次移动一个玩家的标记)或添加玩家决策而不是随机元素(游戏邦注:例如玩家可以从特定移动选择范围中做出选择)。玩家决策不只是复杂的思考决策,同时也可能是瞬间发生且基于灵巧性的决策(例如《吉他英雄》中的弹奏技能)。

我们的教科书划分了3种类型的运气/技能游戏:

1.机遇游戏。可以是儿童游戏或博彩有戏。可以通过添加策略元素到游戏中去强化它们。一些关于技能的错觉就足以让这类型游戏变得更有趣了。

2.收缩技能游戏。这类型游戏侧重于灵巧性挑战。它们并不大会使用机会元素,反而更常添加一些策略选择。任何能够保持游戏流的内容都可能被添加到游戏中。

3.策略技能游戏。这些游戏让人觉得更紧张且进展更慢,因为它们需要玩家进行思考。添加收缩元素可以打断这些较长的策略环节。许多冗长的RPG便突出了一些较小的收缩迷你游戏(如《天际》中的撬锁内容)去中断一些较长的内容。

技能类型

Jesse Schell在他的《Art of Game Design》中区分了3种主要的技能类型。需要注意的是许多游戏都需要混合这些不同的技能,而这些策略也只是提供了一个起点:

1.实体技能:像灵巧,协调,力量和耐力等技能。这类型技能经常出现于体育类游戏中。然而有些人可能认为一些电子竞技中正确的键盘按压和控制器系列也应该被归入这一类别中。

心理技能:像观察,记忆和解决谜题等技能。这些技能通常与在游戏中做出有趣的决定相关,而最有趣的决定通常都是策略性决定。

3.社交技能:像了解对手,欺骗对手,与队友协作等等技能。这些技能与玩家交朋友并影响游戏中其他人有关。它们通常与玩家的写作技能相联系。这同样也常出现与基于团队的体育游戏中。

Schell同样也区分了真正的技能(游戏邦注:即你在基于某种方式控制游戏时作为人类的真正技能)以及虚拟技能(及与你的游戏角色在游戏中做某事的技能)。真正的技能只有在你运行时才能得到提高,而即使你的真正技能未能得到提高你的虚拟技能也能够不断完善。实际上,Schell列出了你在游戏中可能会使用的所有技能并将游戏分解成一些技能组件。你可以从中找到最适合你们玩家的技能,从而成为一名更出色的设计师。

机会

机会可以使一款游戏变得更有趣,因为它能够往游戏中添加一些不确定因素。未知的惊喜能够带给那些喜欢惊喜的玩家乐趣。机会同样也与游戏中的概率具有直接联系,Schell列出了游戏设计师将会很熟悉的10大概率规则:

1.派别是小数是百分比。派别,小数,百分比都具有相同作用并且都是一样的:就像1/2=0.5=50。作为人类,我们总是喜欢使用概率。

2.0到1。当然了这也是关于概率,即发生于0到1之间(如100%)。当我们说到游戏中的概率时,像-10%或110%这样的可能性是不存在的。如果你尝试着计算掷筛子的可能性,并且结果高于100,那你可能就需要重新计算了。

3.“渴望”除以“可能结果”等于概率。概率其实是你用你所期待出现的结果数除以可能出现的结果数(这种情况下可能出现任何结果)。

4.列举。让我们假设你尝试着寻找你想要找到的结果,并且这不如D6那么直接;获得你想要的答案的一种有效方法便是列出你所处情境中可能出现的任何结果。这能够帮助你明确模式与组合。

5.在特定情况下,OR意味着ADD。当你尝试着明确x或y发生的可能性时(如绘制桥牌上的特定纸牌),这些事件通常都是互相排斥的,你可以添加概率去获得一个OR事件的所有概率。

6.在特定情况下,AND意味着乘以。当我们正在寻找两种事物同时发生的概率时,我们可以乘以它们发生的概率。这只能作用于两个事件不会互相排斥的情况下。

7.1减去某数可以是“does”也可以是“doesn’t”。当1代表的是某事发生的100%机率时这种情况是符合逻辑的。所以不管何时当你在计算某种情况发生的可能性时,你便可以用1减去这一数字以获得对立面事件发生的可能性。

8.多元线性随机选择的综合并不等于线性随机选择。线性随机选择指的是所有结果都具有同等出现机率的一个随机事件。掷筛子便是一个很好的例子。添加多次掷筛子并不意味着可能结果拥有同等的出现机率。掷两次筛子意味着某一面会出现的机率变得更高。但是这种情况的可能结果都是遵循一个可能性概率分布,即中间数值(6,7,8)拥有更高的出现机率。

9.掷筛子。Schell区分了理论概率和实际概率。理论概率便是我们到目前为止所聊到的内容。这是一般情况下会发生的事。而实际概率则是指代已经发生的事。即你可以反复滚动筛子并记录下你所获得的数字,然后基于此去计算概率。最理想的情况下,这一概率非常接近理论概率。这也是我们所谓的蒙特卡洛法。

10.Gombauld定律。Shcell建议和朋友一起进行计算,不管何时当你面对一个概率问题时总是很难独自解决。这可能包括在邮件列表上刊登与数学和概率相关的问题。

以下是关于机会的一些重要内容(源自Adams和Rollings):

谨慎地使用机会元素。

基于较小的风险和奖励而频繁地使用机会元素。

让玩家按照自己的情况选择使用机会。

让玩家能够决定风险的概率。

篇目3,解构游戏设计中的随机性与概率问题

作者:Keith Burgun

很久很久以前,我们便已经依赖于各种类型的随机性去帮助我们的互动系统的运行。尽管在所有类型的互动系统中总是存在随机性的一席之地,但我认为现在关于策略游戏的随机性的假设却是错误的。

我想强调的是充斥于玩家的选择与结果(在这里便是创造出随机性)之间的噪音并不会被归入策略游戏中。

什么是随机性?

基于本文的目的,随机性指的是“不可预测并且会影响游戏状态的信息。”随机信息的生成过程是人类永远都不可能想到的。随机系统的经典例子便是掷筛子,洗牌或随机数生成程序。

从技术上来看,掷筛子模式并不是真正的“随机”。这只是对于实体的回应,计算机可以获得丢筛子的信息并预测将会出现怎样的数字。而我们会提到筛子是因为人类并不能像计算机那样做。实际上,当我们将筛子整合到游戏设计时,我们便是假设没有人能够(或不可能)预测到结果。

实际上,尝试着预测筛子将如何滚动,并基于一个计划好的轨道去投掷它从而出现你想要的一面的做法被观察者们当成是一种“作弊”手段。你不应该知道筛子的整体理念。

一部分原因是我们正在面对包含随机性的游戏中两个独立的封闭系统。滚动筛子是一个封闭系统,与优秀的游戏系统并无关系。

这与其它类型的“不可预测”或“不确定”的事是不同的。就像象棋吧,它会限制玩家能够预测的圈数。此外,会出现什么事也是玩家预测不到的。然而,玩家可以通过游戏而不断学习并进一步分析可能性。我们可以使用部分象棋技能去探索不断增加的可能性空间并创造出更加可预测的能力。

所以尽管象棋具有不可预测性,但却不能说它具有随机性。为了运行,所有游戏都必须具有某种类型的不可预测性,但随机性却不是帮助它们做到这点的唯一方法。相机的不可预测性的来源与随机性的来源是不同的,因为玩家可能逐步减少并理解这种不可预测性。

随机性的类型

随机性可以被划分成两种类别:输入随机性和输出随机性。

输出随机性—-当我们想到游戏中的随机性时,它们便是输出随机性。输出随机性夹在玩家决策和结果之间。例如“Risk”(游戏邦注:古代流传下来的征服世界的游戏)或《Memoir ’44》中的掷筛子战斗,以及《X-Com》或《FTL》中的随机数字生成战斗。我将把不具有这类型随机性的系统称为“确定性”。

输入随机性—-这种类型的随机性会在玩家做决定前给予他们暗示。典型的输入随机性是《文明》或《Rogue》中的地图生成,或《Puerto Rico》或《Agricola》等职工安置游戏中的面朝上的砖块或纸牌。(人们经常使用“程序生成”这一词去指代数字游戏中的这类型随机性。)本文将不讨论这类型随机性,但是你们有必要了解它们的区别。

有趣的是,尽管这两种类型是截然不同的,但从技术上来看它们却是处于一个连续统一体中。在此我们需要注意的是,不负责任地使用输入随机性将会引起与输出随机性所遭遇的同样问题。

策略游戏学习引擎

策略游戏是一种让我们能够理解的引擎。就像我们玩一款游戏,不管获胜或失败,我们都是相连接的。当我们清楚系统如何运行时我们便会说“喔,我知道了!”为了发展,我们发现这一过程非常有价值且具有娱乐性。这是策略游戏的“必要乐趣”。

让我们进一步分解这一过程。

告知玩家—-玩家着眼于游戏状态,尝试着明确该如何行动。游戏通过他的“技能”基础提供给他信息—-这是关于系统以及它是如何运行的整体观察内容。

决定行动—-在剩下的游戏过程中,系统将对这一输入内容做出回应。在决定后将发生一系列事件,包括最终的胜利/失败;所有的实践都将作为游戏对于玩家的反馈,突出它们之间的一些休闲的关系。反馈同样也遵循着一个策略。

记录技能—-玩家将观察并记录这种因果关系并将其记录到自己的数据库中。之后玩家便可以使用技能而行动。(需要注意的是,这时候便会出现策略游戏的必要乐趣,但这当然也是依赖于其余功能。)

当一名玩家在玩游戏时,他将创造这种“技能文件夹”并变成一名强大的玩家。一款肤浅游戏中可能不具有多少这样的时刻,而一款具有深度的游戏将能够持续传达这样的时刻。这便是为何我们会基于策略上的“深度”去衡量一款游戏的质量。

那么我们该如何达到这种深度呢?首先,也是所有游戏设计师都意识到的意外的复杂性。为了创造复杂性,我们让游戏能够在玩家的游戏过程中生成复杂的意外情境。主教,骑士和赌棍与三个士兵和一名女王相对坑并不具有内在的复杂性;这里存在少量的可能数据。然而如果在棋盘上同时出发这两边势力,那么便可能出现许多不同的情境。

复杂性效率

而关于达到这种深度的第二种方法却还未被大多数设计师所察觉。这种方法包括识别复杂性的有效性:状态和过去状态的历史之间的相关性。

策略游戏在比赛过程中只拥有有限的状态。据我所知,象棋游戏中的平均数值是在40左右。而实时游戏并未真正分离“回合”,也仍然存在一些有限的有意义的状态,不管你如何进行划分。

如果你的游戏是由一系列相互联系的事件所组成,那么你便能够最大化可能出现的独特情境。我认为这一理念违背了许多认为随机事件的出现将提高独特情境的数量的人的想法。然而这却是事实。

拥有一个系统将能够推动你的意外复杂性达到最高效率。因为每种意外情境都考虑到了之前和之后出现的所有事件的最大差别量。

deterministic(from gamasutra)

deterministic(from gamasutra)

这里的粉色的蛋代表当前的游戏状态。在决定性游戏中,玩家将顺着比赛的时间线通过不同时间获得它们。而在随机性游戏中,时间线是相互独立的,即当前的游戏状态并不会受到影响。

在决定性游戏中,当前的游戏状态与整体时间线的每一部分是相互联系着。正因为这样,它被整合到了更复杂且更独特的结构中。这是以环境的形式表现出来,并提供了有关游戏状态的解释。

当然了,具有更高随机性的游戏拥有一些决定性元素能够提供给游戏状态一些环境。例如在像《召唤师之战》(游戏邦注:包括掷筛子战斗的回合制战斗游戏)等游戏中,你的召唤师的生命值以及单位的位置都是相互联系的,并且能够为游戏状态提供一些环境。

然而,游戏中的大量环境信息将不再具有意义。我攻击了你的单位,我滚动了筛子。我有可能会错过机会,而下一个回合你便会杀死我的单位。这一事件(即你杀死我的单位)并不是真正与我之前采取的任何行动随机联系在一起。这里所发生的情况是,我采取一个行动,然后随机发生了一些事,然后你便采取行动。这种关系是相互独立的,我们不能再使用我的行动作为当前游戏状态的环境差别。现在你的游戏已经不再是“A,因此出现B,因此出现C”。而变成“A,然后出现B,然后出现C”。

最重要的反馈是目标状态。一旦一场比赛结束,胜负条件将通过事件的发生过程传送一个命令,并为促成这一结果的每个事件揭示一个积极或消极的命令。这一行动还不错,因为它能够引出这一结果,并且这一结果能够引出其它结果,并因此成就了我的胜利。

这并不是说当玩家获胜时,他的所有行动都是正确的行动。然而这却提供了一个定位点能够传达每个其它行动。当然了,玩家的每一个行动都是为了更加接近获胜状态。一旦比赛结束,我们便能够看到这些行动是否有效及其原因。(正因为如此,玩家能够从观看回放并分析而获得许多同样的乐趣。)

总的来说,在玩了一款决定性游戏后,玩家可能会看到基于时间轴的一副连贯的战略图。而玩家可能会认为非决定性游戏是由一些不完整的图片所组成。基于这种方式,决定性游戏能够最大化其复杂性效率,而非决定性游戏却不能。就像非决定性游戏在添加复杂性的同时,决定性游戏则在加倍繁殖这些复杂性。

想象的深度

输出随机性并不能提升游戏的深度。在掷筛子中我们探索不到任何内容。我们只知道任何一面出现的几率是1/6。除此之外别无所知。

实际上这也模糊了结果。你可能玩得很好,但却仍然输掉了游戏。游戏让你做了徒劳无益的事,想想你是在哪个环节搞砸了,在什么时候你的游戏其实并不具有任何问题;这里的关键在于掷筛子。

因为徒劳无益,游戏似乎变得更加复杂了。游戏提供了不可靠的反馈,只有在玩了无数次游戏后你才清楚自己该忽视哪些反馈。从根本上来看,随机游戏因为将错误的信号整合到引擎中而延迟了学习过程(这也是游戏的必要乐趣)。这是创造深度表象的超级廉价的方法,这也是游戏设计师会被吸引的原因。

人类是追求模式化的动物。我们会观察云端中的数据,我们会观察静态的图像,我们会观察存在巧合的阴谋。这都是因为人类的进化不断推动着我们基于这种方式进行思考。同样的原因也导致人们认为自己在一些灌木丛中看到的影子是狮子等野兽。随着时间的发展,那些认为自己看到狮子的人变成了那些在真正看到狮子时会逃跑的人。而我们也继承了这些人的基因。

基于这一原因或其它原因,现在的我们会注重我们所看到的任何模式,并且只要存在游戏,游戏设计师便会一直利用这些模式。

就像赌博机一直都是利用心理技巧去引诱人们进行尝试。为了让任何人想要玩与老虎机或轮盘赌一样无趣的游戏,于是便出现了某种程度的自我欺骗行为。在某种程度上,玩家会觉得如果自己获胜了,那么功臣便是自己。否则他们怎么会倾注于此呢?从古代的宗教迷信到一些更加现代的想法,如“在筛子上吹口气”,或轻吻“幸运”物,或其它自欺欺人的赌徒谬误,我们发现为这些事件分配意义的方法其实就是在制造噪音。

具有较高随机性的策略游戏中的认真的玩家会在玩《召唤师之战》和《Hearthstones》时怀疑同样的技巧是否能够发挥作用。但为什么呢?如果玩家能够在不具有任何策略的系统中执行这一技巧,他们应该很容易相信这样的技巧能够作用于整体系统中的一部分内容。实际上,整合随机元素到一款策略游戏中能够让它更轻松地合并噪音和策略反馈,因为游戏中所发生的一些事件是真的具有策略性和决定性!

在这些游戏中存在一些真正的游戏技能,但同时也有一定量额外的“幻想技能”,并且正是这些幻想技能让游戏变得比实际更具深度。实际上,大多数玩家会利用系统去快速解决某些内容,而随机性便是其中的决定性元素。

反论

我研究了这一问题好几年了,而随着时间的发展我遇到了一些反论。

游戏设计师兼博主Danc(他的网站是Lost Garden)在多次回应我的论据时都是这么对我说的:“输出随机性只是下一回合的输入随机性。”从根本上来看他认为输出随机性和输入随机性之间不存在真正的区别。

该论据带有两个主要的缺陷。一个缺陷是它好像未察觉到更大的策略视图的可能性,即能够提供你正在错过的许多复杂性效率。

另外一个缺陷便是即使那输出随机性真的是下一回合的输入随机性,这也是我所谓的“不公平的输入随机性”。它们如此接近你,从而导致你根本没有时间做出回应。现在你拥有完全不同的游戏状态,但却不存在可识别的原因。在某些游戏中,你可能玩得很好,但如果整合了这一论据,你可能就会遭遇失败。在其它游戏中,你可能会因为掷筛子而未利用这一论据。当游戏向玩家近距离提供输入随机性而导致他们无法做出规划时,它便等同于输出随机性。所以反馈是因为人为原因而延迟。

讽刺的是,我同意Dan关于输出随机性与下一回合的输入随机性之间不存在重要区别的观点,尽管我认为它们同样很糟糕。

为了把事情讲清楚,让我们想象你的角色拥有一个“命中的”筛子去对抗一个强大的怪兽。他滚动了筛子,但却错过了机会。没关系,毕竟这只是下一回合的输入随机性。他尝试着再次攻击,但却再次错过了机会!这时候,你可能已经输了,而这并不是因为你做出的任何决定所导致的。

“有些游戏需要输出随机性才能运行。”

如果你因为风险而抛弃了筛子滚动,游戏便不能够运行。

这意味着它们是些肤浅的游戏。这是可被理解的,因为创造具有深度的统一系统是件非常非常困难的事。然而这并不是对于随机性的辩护;这反而是在暗示你的设计有多糟糕。

“如果存在随机性,那么这便全部是关于风险管理。”

这一论据背后的理念是拥有随机元素将在游戏中添加“整合你的几率”的元素。你必须衡量结果A的发生几率与结果B的发生几率,以及结果A的利益与结果B的利益,这能让游戏变得更有趣。从根本上来看这结合了几率与估值。

这种类型的风险管理并不局限于随机游戏中。在你还未解决的任何游戏中,你所作出的每个行动在某种程度上看来都是你必须管理的风险。例如在象棋中可能存在两大策略—-策略A和策略B,但策略B拥有比策略A更高的报酬。那么这时候随机性便是不必要的。

关于“估算几率”这方面,决定几率从来都不是什么有趣的事,特别是当你在谈论像扑克游戏中的计算纸牌时。在决定性系统中估算几率可能更加困难,但是因为在一款有趣且动态的策略游戏中存在各种变量,所以这种估算变得比较有趣了。

“随机性并不重要—-你只需要尽自己所能做到最好!”

这里的论据就像是:“如果你在乎随机性,你便太过在乎输赢了。只要好好享受乐趣就好!”

这一论据并不是在为策略游戏中的随机性辩护;相反地,这是在为玩具中的随机性辩护。策略游戏拥有输赢的条件。如果你告诉我们在《FTL》中忽视这点,那就等于你在说《FTL》是个玩具。

“担忧更广泛技能范围的玩家能够彼此对抗。”

如果一位大师和一位新手一起玩象棋,那么结果便不会有趣,或者只能让其中一方感到满足。这一论据告诉我们应该丢些随机性到游戏中。

当然了,这就像将孩子与洗澡水一起倒掉一样。为了让玩家觉得自己是与同样技能水平的人一起游戏,你已经严重破坏了游戏。而关于这一问题的真正答案应该是适当地安排比赛。

“随机性将让游戏变得更像现实生活。”

为了快速反击这一论据,让我们假设策略游戏中存在一组价值,我们可以将其与模拟游戏中的价值区别开来。

“带有随机性的游戏仍然带有技能!”

没错,我也不会否认。但是问题在于,在实际情况下你将探索更少的领域,因为许多游戏都浪费在了错误的随机结果中。

其它反馈

我应该注意的是一些输出随机性类型并未被如此看待,但是因为具有相似的功能,所以它们也带有同样的问题。

同步行动—-例如在RPS中尝试着猜测敌人将做些什么便是一种有效的随机设定。实际上,这也是我们为何会使用它去决定谁清除了垃圾,我们认为这是公平的,因为这是随机的。整体原因是人们同意使用RPS作为谁清除了垃圾的决定元素,因为他们知道他们或者对手都没有办法去提升自己的几率。

执行—-游戏中的执行是关于“可以”,而不是“应该”。你是否能在我跳起来踢你的脸前按压这系列按键?也许执行仍然胜于随机性,因为你至少是擅长它的。然而在一场单独的比赛中,它们却几乎相同。“你想要做什么”以及“你的身体是否能够实现欲望的输入”之间所涉及的复杂的化学元素,紧张感,肌肉和组织等等都带有许多错误的空间。当你选择为你的《Dragon Punch》创造输入内容时,它是否真的可行?这便是有效的随机性。

结论

我们所收集到的有关游戏设计的随机性观点其实是长久以来都未发生多大改变的内容。而这时候我们真正需要做的便是更加认真地去思考这一问题。

我并不是在说游戏设计中不存在任何类型的随机性的存在空间。实际上,我真的非常支持多人游戏中平衡且没有太多变化的输入随机性。并且单人游戏也需要输入随机性。

然而我们却应该避免基于各种形式的输出随机性。你只有在创造博彩游戏或不满足系统深度的时候才能够使用这种类型的随机性。

篇目4,阐述游戏中的运气元素类型及其作用

作者:Noel

游戏中运气元素的数量和类型会对游戏的整体感觉产生深远影响。有些游戏根本就没有运气元素,所有的变化均来自对手的行为(例如象棋),有些游戏则全部取决于运气(例如轮盘赌),多数游戏则介于两者之间,从而创造了无数种体验。

在此我们不打算过多探讨运气在电子游戏中的作用,因为它隐藏在计算机模拟的黑盒中,但与桌游一样,它可能对电子游戏所提供的体验产生极大影响。

对于我正在制作的游戏来说,运气是一个重要元素。我们制定了一些重要决定,这里涉及运气如何成为游戏的一部分,以及它能够为玩家创造哪种体验。希望本文对面临相似设计挑战的人有所帮助。

本文适用于任何类型的游戏(游戏邦注:包括桌游和电子游戏)。在此,我对运气的定义是植入游戏系统本身的随机效果,而不只是玩家互动。

非运气游戏

在不含运气的游戏中,玩家完全依靠技能取胜。这种游戏类似于体育项目。游戏就变成了一种紧张、直接竞争、考验玩家大脑的活动。这可以看出运气究竟会对游戏产生什么特殊影响。

非运气游戏的绝佳典型就是象棋或围棋等游戏。此外,《Puerto Rico》、《Caylus》等现代桌游(它们的最初布局就极少含有运气元素)也属于这种类型。

有趣的是,许多抽象游戏通常都不含运气元素,而游戏主题越明确,就越需要碰运气。

你幸运吗?

对多数桌游来说,含有一些运气元素对它们来说非常有好处。例如:

1.让游戏显得与众不同。

2.让玩家觉得自己有机会赢,即使自己现在并不领先。

3.移除获胜玩家所背负的心理压力(例如,“假如有人打败我,那也是因为他运气好罢了。”)

4.让没有赢的玩家觉得自己下次还有机会扳回一局(“下次我会来个绝地大反弹!”)

以上的第2、3、4点都有助于鼓励更多玩家参与游戏,让他们觉得自己有竞争力,即使现在没赢(或者即使他们实际上并没有竞争力)。这方面的一个例子就是扑克:每个人都觉得如果自己得到好牌,就能够打一手好牌。实际上,从长远来看并非如此,但扑克引进了不少短期内的确能够见效的运气元素。

上述几点总合起来的好处就在于,可以让不同技能水平的玩家参与到同一款游戏中,共同获得乐趣。对于那些需要多人参与的游戏来说,运气元素甚为关键。

运气的类型

至于那些添加了一些运气元素的游戏,它们可以选择不同的运气数量和类型创造不同效果。不幸的是,这也有可能因为混合了错误的运气类型,而创造了一种令人抓狂的体验。

*事后运气。这种运气要在玩家已经做出决定并执行一项操作后才会出现。它可能是通过抛硬币来决定玩家能否解琐一个箱子,或者摇骰子来决定你的敌人是否进攻一个国家。

*事前运气。事先运气包括玩家执行一项操作之前发生的随机事件。玩家可以先考虑这一因素再做决定。

*隐藏信息:隐藏信息是第三种运气类型。我有点犹豫到底该不该其单独分类,但它看起来又与其他两类不同。隐藏信息是指那些只有一些玩家知道,并且会影响其他玩家或者游戏积分情况的东西。

dice-troyes(from gamesfromwithin.com)

dice-troyes(from gamesfromwithin.com)

事后运气

我并不推崇事后运气。玩家已经执行了操作,但结果却是随机的(例如摇3颗6面的骰子)。这并不会增加玩家所拥有的选择,这在多数情况下并不有趣。这是一种可以为无聊的游戏增加一点特色的运气,但并不会让游戏变得更有趣。

如果使用不当,这种运气会极为令人抓狂。玩家会觉得自己选择的是“最佳”操作,但摇出来的结果却是适得其反。当然,也可能出现一些令其兴奋的结果,但这真的好玩吗?也许第一二次还行,但之后就很难说了。

通常我并不喜欢在自己的游戏中运用这种运气,但也有一些情况下,我还是会将其添加到游戏中。

第一种情况,当玩家可以在两项操作中进行选择,并且知道这两种操作的不同难度时。你选择掷一次骰子,重创敌人;也可以掷两次骰子,但如果两次的结果都是1,那么受伤的就是你的角色。在这种情况下,尽管这仍然是一种事后运气,玩家也需要提前做出有意义的决定,并且要衡量两种选择的利弊。

第二种情况,当玩家在游戏过程中多次重复某项操作时。此时每回操作本身的结果都没有什么变化,所有操作最终会在游戏过程中趋于平庸。这时引进的运气元素可以为游戏带来一点变化,在不影响游戏环节的前提下制造一点兴奋感。

如果玩家在游戏过程中能够慢慢改变其概率曲线,再结合这些运气元素,就可以随着游戏发展增加某项操作的成功机率,从而令玩家觉得自己变得更强大。这种做法常见于RPG和电子游戏。

添加一些影响操作结果的事后运气还可以给予玩家希望,让他们觉得自己能够取得成就,即使成功机率如此之小。而如果没有任何运气元素,他们就会觉得毫无希望,并对游戏丧失兴趣。与此同时,运气元素的存在也让玩家无法预测操作结果,这便于玩家无需耗心思去猜测结果而做出决定。

最后一种适用事后运气元素的情况就是非常短小的游戏。我喜欢《King of Tokyo》,尽管它完成是一场骰子游戏,含有大量事后运气元素。即使你真的只得到一些很糟糕的点数,这款游戏也不过10-15分钟,不会让你觉得浪费时间。而如果你在一个骰子上投入4个小时,那就真的太不值得了。

事后运气的负作用表现在人类对随机奖励的成瘾性,这也正是赌博和老虎机如此受欢迎的原因。游戏可以借用人类弱点来吸引玩家体验原本并不是非常有趣的活动。

有一种事后运气是购买收藏卡牌游戏的“提升牌组”(例如《万智牌》。购买纸牌是执行操作,但你打开时看到的牌却是随机的。相信多数《万智牌》玩家都可以作证,这种设计极富成瘾性。

事前运气

这种运气像事后运气一样可以增加许多随机性,但却可为游戏创造一种截然不同的体验。由于随机事件是发生于玩家行动之前,所以如果你觉得自己并没有得到理想的结果,你就可以选择在下次行动之前发挥最好的表现。

要说明这两种运气的不同,我们可以看看第一人称射击游戏中的加成道具。你打开门进入一个房间,看到一个神秘的礼盒。你不知道那是什么,打开看看发现是命值加成。如果此时你恰好命值不足,那就是个好事。也有可能你命值已满,所以这就是无用之物。这就是所谓的事后运气。

另外,还可以想象你打开房门,看到3个加成道具并排放在那里。你看得到它们各自的作用(命值、弹药和新武器)。你只要拿起其中一件,其他几个就会消失。这几个选择未必都很理想,但你可以根据自己当时的情况做出决定。这就是所谓的事前运气。

在桌游中,Stefan Feld可以说是事前运气的大师。他的许多游戏都包含此类限制你操作的运气机制。例如,在《The Castles of Burgundy》或者《Bora Bora》中,你掷骰子,并由这些骰子上的数字来决定你可以采取的行动。

任何与纸牌有关的游戏或多或少都会使用事前运气。你所拥有的纸牌是事前运气,之后你就要尽自己最大努力打一手好牌。

关于事先运气的一个极端例子就是最初游戏布局。这在游戏中只会发生一次,并且是在玩家采取行动之前,所以它有可能影响整个游戏过程。即使坚决反对游戏运气元素的玩家,通常也很容易接受这种随机性,因为他们可以在游戏过程中将其考虑入列。

事前运气并不像事后运气那么普遍,但和后者一样适用于多种情况。例如在角色攻击一些怪物时,通过掷骰子来决定是否要进攻,以及将产生多大杀伤力这种场景中,我们可以让玩家掷骰子,并以此来决定他们可以采取的操作,使之变成事先运气。比如,低点数的骰子表明玩家只能执行一些贴近地面的攻击,面高点数的骰子则意味着他们可以攻击高处飞的敌人。之后,玩家可以选择自己能够采取的攻击行为,或者采取防御姿态,或者溜之大吉。

事先运气的一个弊端在于,它会延伸每位玩家的操作。使用得越多,其呈现给玩家的选择就越多,游戏所需的时间可能就越长,所以最好将其运用于需要做决定的时候。如果不是,最好使用事后运气或不要采用运气元素。

隐藏信息

桌游的一个普遍例子就是隐藏游戏奖励。例如,在《Shipyard》中,玩家会实现一系列最终会让自己得分的目标。这些目标的存在原因有二:通过给予玩家不同目标,鼓励玩家专注于游戏的不同层面,而不只是重复同一系列的“最优”行动。它还鼓励玩家关注其他玩家的行动,并试图阻其他玩家过于领先。

另一个更有趣的例子是《Troyes》。每位玩家可通过一系列终极游戏目标获得额外点数,并且所有玩家还将根据这些目标获得分数。这会让玩家关注其他人的表现,并让这种行为变得更有意义。

隐藏信息的一个极端例子是《Discworld:Ankh-Morpork》,每位玩家在其中都有一个隐藏的获胜条件。在有人宣布自己获胜之前,大家都会各行其事,并亮出自己隐藏的胜利条件底牌。

隐藏信息越是重要,游戏的休闲和随机性就越强(因此游戏就可能越短)。

篇目5,从游戏开发角度探讨随机性元素的使用

作者:Darran Jamieson

游戏的运气vs技能元素可以说是优秀设计的核心—-这是我们在之前所讨论的内容。但在我们担心如何平衡运气与技能时,我们真正需要问自己的是:什么是机会,在游戏中需要呈现什么程度的机会?此外,我们该如何让以奖励而不是惩罚的形式去执行机会,并使用它去完善整体的游戏体验?

是否需要机会?

我们几乎不可能创造出一款没有运气元素的游戏。没有运气元素的游戏并不算真正的游戏—-这就像是“谁是最高”或“谁拥有最长手指”并未包含任何挑战一样。这是玩家不能改变的最简单的测量方式,并且不可能提供任何娱乐。游戏必须拥有不确定性元素—-就像“谁能够测量出最长的退”,至少这是未事先决定好的事。即使当一名玩家比别人优秀,这也不足以保证他的成功。

我们在许多游戏中使用纸牌,筛子或随机数字生成去创造这种不可预知性。但并非所有游戏都使用了随机工具,像象棋等注重策略的游戏便需要随机元素:这些元素是源自玩家。玩家是不可预知的对象,并且经常会根据自己所预测的最佳结果而改变策略和战术。这便是为何作为一款静态游戏,象棋仍具有很多动态性:因为不存在任何两名玩家会基于同样的方式玩游戏。

人们可以提供机会去玩象棋是因为象棋是非常复杂的,实际上,我们可以将象棋当成是一款复杂的游戏。不幸的是,与“流”或“零和竞赛”理念不同的是,“复杂游戏”这一词并不是一个公认术语。因为我们将频繁地提到复杂这一词,所以我们可以自己对它进行定义。

理解复杂性

一字棋便是一款带有非常简单的规则的游戏。游戏中有9个空间,玩家将操控X或O,尝试着创造一条直线,并且通常移动9次以下便能够结束游戏。我们总是很容易预测一字棋游戏的结果,甚至是在第一次移动之前—-假设两名玩家都准确地玩着游戏,那么游戏有可能是以平局结束。

可以说一字棋并不属于复杂游戏。实际上,我们已经摸清了一字棋的套路,即我们估算出了游戏中每一组可能的移动以及最佳移动组合。更糟糕的是,人们甚至可以无需动用过多脑力便能够“解决”一字棋游戏。

让我们将其与象棋进行比较。象棋带有64个格子并且有6种不同类型的格子,每种类型都有自己的移动组合,并且还包含像王车易位等特殊的移动,这意味着游戏可以永久地持续下去。考虑到这些情况,对于象棋可能是永远都解不开的游戏(甚至当对手是最强大的计算机时)这一点我们一点都不惊讶。

所以从本质上看,复杂游戏是指那些未被解决,或者玩家解决不了的游戏。

“玩家解决不了”这点非常重要。这意味着游戏将持续具有乐趣,因为玩家解决不了其中的谜题。这便是四子连环棋始终受欢迎的原因;尽管计算机能够解开这款游戏,但是玩家却做不到这点。而虽然对于大多数玩家来说一字棋很简单,但对于那些不知道如何规划每一步的孩子来说,它仍算是一款优秀的游戏。所以说复杂性应该是一种主观的看法。

那它到底有何重要性?因为一款不复杂的游戏也就等于一款无聊的游戏。如果游戏一点都不复杂,那么它便很容易解决。如果它被轻松解决了,那么结果便是可预测的;所有玩家需要做的便是计划出最佳移动组合,如此他们便能获胜。这样的话玩家便有可能离开游戏。

边注:我们也可以将一款可被解决的游戏称做一个谜题。尽管谜题很受欢迎,但是直到被解决的时候它才具有乐趣—-这便是为何喜欢字谜游戏的人不会一遍又一遍地坐着解决同一个字谜。决定创造一款益智游戏当然没什么错,但是你必须清楚你的目标是什么,以及这会如何影响游戏的重玩价值。

命运的工具

所以当我们着眼于像象棋或一字棋这样的游戏时,我们可以发现它们都是不具有嵌入式随机性的策略游戏:基本的策略游戏。然而也有许多游戏使用筛子,纸牌或其它工具作为嵌入式机制,如蛇梯棋或扑克。虽然大多数这类型游戏都称不上复杂,但包含筛子或纸牌却是避免游戏轻松被解决的必要元素。在蛇梯棋游戏中,如果玩家每轮中在1至6个格子间选择一个数字进行移动,那么即使是孩子也知道“总是选择6”会是一个最佳策略。

当然了,添加随机性并不意味着能够拯救一款可被解决的游戏。实际上,你可以将目标从“找到解决方法”改成“找到最有可能的解决”。这时候你仍然拥有一款益智游戏,不过获胜条件便没有保证了。太多随机性也不好;蛇梯棋便是一个显著例子。如果只有孩子在玩一款游戏,这便说明游戏缺少任何互动挑战,所以人们会认为这是毫无意义的游戏。

所以为何像扑克这样的游戏能够持续受到欢迎?从根本上来看,扑克是由一系列迷你谜题所组成。你将获得帮助,并需要“解决”这一帮助对于你获胜的可能性。然后你可以根据你获胜的可能性下赌注。

这是对于扑克最简单化的看法,如果这便是这类型游戏的全部的话,它便太无聊了。编写一个程序去评估可能性并运行它去最大化你的获胜机会其实一点都不重要。

扑克的乐趣源自玩家的互动:源自欺骗与自信。你不需要做最好的押注,你也可以往一些没有获胜可能性的内容下赌注。实际上这便是扑克游戏的本质;纸牌只是起到推动作用,并提供全新回合的内容。通过添加随机元素,我们消除了玩家的一些了解,这意味着我们可以使用未知性作为游戏机制。玩家需要基于他们所知道的采取行动,这便是估算获胜几率以及依靠机智战胜其他玩家的结合。

无聊的桥牌

所以,尽管我们谈论的是传统游戏,但计算机游戏也使用了同样的设计原则。像《俄罗斯方块》或《宝石迷阵闪电战》也可以说是带有额外随机性的简单游戏,而像《星际争霸》和《军团要塞》则是复杂游戏。

在几乎所有的游戏中都存在一定的谜题元素。甚至是在RTS或FPS中,玩家也会基于最优游戏玩法不断做出决策:我是应该创建坦克还是飞机?我是应该选择机械枪还是榴弹发射器?我是应该向左还是向右?就像在象棋中,玩家会根据自己的想法是否会创造出最佳结果而做出决定。随机性并不是计算机筛子所创造的,而是由玩家在游戏中所作出的选择所创造的。玩家既尝试着以智取胜,同时也尝试着以技取胜。

实际上,我们可以认为PvP游戏(如FPS或RTS)中的唯一随机元素是来自玩家本身。就像我们之前所讨论的,《军团要塞2》中的暴击是玩家基础的争论焦点—-总的来说,《军团要塞2》中的任何射击都有可能是暴击。暴击需要随机性,而任何由子弹所带来的伤害都会比正常标准强上2,3倍,这便导致暴击成为了致命因子。尽管新玩家喜欢随机杀戮带来的刺激感,但是“持赞成态度的”玩家认为暴击机制对他们的技能是不利的。

我们使用随机数的范围对于结果也具有很大影响。如果步枪每次发射所创造的伤害值是90至110,而我们拥有150个生命值,那么不管发生什么,我们只要被射中两次便会死掉。然而,如果我们拥有100个生命值,那么步枪随机杀死我们的几率便缩减了一半。尽管随机性的范围较小,但使用量却非常重要。

随机性的影响

所以为什么“持赞成态度的”玩家会惋惜暴击系统而“休闲”玩家并不会?答案便是取决于玩家的期待。

持赞成态度的玩家会更频繁地玩自己所选择的游戏。他们会更深入地了解游戏。了解自己受到了什么伤害,能够分配什么,以及任何情景会出现什么结果等等。他们有时候不能有效地做出判断,而这通常是因为低估了对手的技能或者做出了糟糕的瞬间选择。

所以当持赞成态度的玩家进入一场战斗时,如果毫无理由地被子弹击中,他们便会觉得自己上当了。他们知道自己期待发生什么,但是因为随机的筛子滚动,他们很有可能马上就会被杀掉。

而新玩家通常都不会因此而受伤;因为他们不是很了解游戏,也并未期待这可能发生什么,所以他们进入一场战斗后也不一定会期待着自己能够获胜。对于他们来说,战斗只是像技能测试那样的学习经历罢了。

这种被随机性所破坏的期待经常发生于任何带有随机性的游戏中。当你在《俄罗斯方块》中等着直线砖块掉落时,计算机可能会连续给你6个S形砖块,这时候玩家便会觉得自己被骗了。大受欢迎的《Puzzle Quest》便收到许多玩家关于“骗人的AI”的抱怨;其开发者也到各种论坛上解释了它的AI并不是在骗人。

所以为什么玩家会有这种感受呢?为什么玩家会因为随机掉落的宝石颜色而失落?因为随机性颠覆了玩家的期待。当玩家进入一款游戏时,他们期待着能够接受挑战,但他们同时也期待着,只要自己谨慎游戏的话便能获胜。当游戏随机丢给你一些糟糕的数字时,你便会马上失败,然后你就会觉得自己受骗了。你知道游戏是如何进行的,尽管你付出了最大努力,你仍然失败了—-这并不是因为你缺少技能或敌人拥有更棒的策略,而是因为随机性。这对于大多数玩家来说都是非常郁闷的机制。

这种“颠覆玩家的期待”扩展到了各种游戏中。实际上,游戏中的运气元素越多,玩家便越有可能受挫。RPG便是一个显著的例子,特别是因为带有暴击系统。暴击系统经常被当成一个有趣的额外元素,但通常情况下它们也总是会惩罚玩家。这是因为:

1.玩家总是期待着打败大多数敌人。

2.暴击往战斗中添加了随机性。

3.战斗中的随机性意味着不可预知的结果。

4.因此玩家将在他们认为自己可能失败的战斗中获胜,但更常出现的情况是:

5.玩家将会在他们认为会获胜的战斗中失败。

这便是在假设这种遭遇是针对于玩家进行设定的。有些RPG会让玩家面对巨大的怪物并让他们被怪物杀死;然而作为专业的设计师,我们应该确保游戏是针对玩家所定制的,而不是随便丢出几只飞龙便结束。

还有其它问题是(假设我们仔细设计了战斗)暴击系统过分偏向玩家。想象在经历时间和空间的悲痛旅程后,我们游戏的英雄接近了恶魔,随后恶魔不仅摧毁了他飞机还一下子就将其杀死了。这并不是什么传奇之战,这反而会让玩家觉得乏味与不满。玩家想要挑战,而如果游戏因为随机性未能呈现给他们挑战,他们便不可能得到满足。

在《Puzzle Quest》,不管AI是否真的在撒谎其实并不重要:真正重要的是对于有些玩家来说,他们觉得AI在撒谎。玩家不可能忽视运气元素(因为他们期待着获胜),但如果玩家的胜利被一系列不幸的随机元素夺走的话他们便会觉得不公平。

纠正问题

所以我们该如何纠正问题呢?似乎到目前为止我们所说的都是关于“随机性是糟糕的”。从根本来看这并没错。我们是专业的游戏设计师;我们应该随机做某些事。玩家所作出的每个决策都应该是精心设计的游戏体验的结果,而添加随机性有可能会破坏这一点。

当我们更仔细着眼于这一问题时,我们意识到可以基于两大理由去添加随机性:

为了让结果更加不可预测,或者

为了创造内容。

让我们检验这些理由:

不可预测的结果

就像我们之前所提到的,玩家喜欢赢。

而我们在此所说的是随机性稍微取代了我们对于技能的需求。

因此添加随机性将让缺少技能的玩家有可能战胜出色的玩家。而在没有随机性的游戏中,优秀的玩家总是能够赢过缺少技能的玩家。

因为这点,拥有不可预测性可能成为游戏中一大重要组成部分:这让缺少技能的玩家也能够影响游戏,并变得更出色。如果玩家不断遭遇更厉害的对手并不断失败,他们便有可能快速失去对游戏的兴趣。然而,优秀的玩家通常都不喜欢这种随机性,他们认为这是在贬低自己的技能。

所以该如何纠正这一问题?

一个选择便是Elo等级系统。这将给予玩家的技能提高给他们一个数字:如果打败一个最厉害的对手,你的Elo等级将提升;如果输给新手,你的分数便会下降。这是象棋玩家衡量自己的技能的一种方法,但是许多MOBA(游戏邦注:多人在线战斗游戏,如《英雄联盟》和《魔兽争霸》等)也使用了这一方法,所以当你进入一场战斗时,你将面对一些带有相同技能的对手。有些第一人称射击游戏也使用了这一方法,让玩家能够在一方持续被镇压时重新平衡团队。

另外一个选择便是差点系统。这一系统与Elo系统没有太大的区别,它允许玩家能够基于技能级别而提供给自己一个人为优势。打斗游戏便经常使用这种方法,提供给较弱的玩家可变的健康和破坏输出奖励。尽管差点系统可能不能解决所有的技能失衡问题(游戏邦注:因为在线游戏玩家很容易滥用这一系统),但这却能够帮助休闲玩家更平等地与硬核好友们相抗衡。

在这两种情况下,你可以减少内在游戏元素的随机性,并让玩家作为唯一的随机性制造者。

生成内容

随机生成内容(或游戏元素,如《俄罗斯方块》中掉落的砖块)的问题并不是指我们生成的不可预知的内容;而是指随机元素的特定顺序让玩家感到郁闷。

如果在《俄罗斯方块》中,玩家等待的是直线砖块,但是游戏却一直出现S形砖块,玩家便有理由感到厌烦。尽管这是不大可能发生的事,但仍存在发生的几率。

在其它游戏中,如穿越地牢的RPG游戏,我们可能拥有1%的机会在每次怪物出现时生成一个boss。如果我们碰巧连续生成3个boss,那么玩家便会觉得自己身处一个不可能获胜的战斗中。

同样地,我们可能在整款游戏中都不会生成任何boss。这将导致游戏变得非常简单,或者非常复杂(如果boss能够掉落装备升级道具的话)。不管是哪种情况,随机性都有可能破坏玩家在游戏中的乐趣。

在某些特定情况下,随机性可能会将玩家彻底带离游戏。就像在大受欢迎的纸牌收集游戏《万智牌》中,玩家将创建需要结合土地(能量资源)和咒语去打败敌人的桥牌。土地的使用是重要的平衡机制:一个小精灵可能需要一个土地,而一条巨龙可能需要十个土地。然而,如果玩家碰巧没有土地纸牌,他们便什么都做不了;他们只能呆坐在那里直到对手将其打死。尽管我们能在某种程度上缓解这种情况,但这却是一个非常严重的设计缺陷,即玩家在游戏中的多次失败其实是坏运气的结果,而不是因为自己技不如人。

人们的亏损感大大多于成就感。这是一种有趣的心理状况;我们可以着眼于如下两种情境:

你得到1000美元。我问你是否想要将其投注于抛掷货币:如果正面朝上你将再获得1000美元,但如果背面朝上的话你就输了。但不管你选择什么你都能够获得额外的500美元。

你得到2000美元。我问你是否想要将其投注于抛掷货币:背面朝上的话你就输掉了1000美元,但如果正面朝上的话你则不会输钱。不管怎样,你都能够拿回500美元。

一般情况下,人们倾向于选择第一种情况,尽管其结果都是一样的!(不管你是否选择抛掷货币,不管货币是正面朝上还是背面朝上,你最终获得的钱都是一样的。)

这意味着如果游戏中拥有一个随机奖励或惩罚玩家的机制,那么从心理学角度来看,玩家的亏损感会大于成就感。如果这种冒险是可选择的,那便能够开启额外的游戏玩法渠道,但强制性选择总是会让玩家觉得自己在遭受惩罚。

关于随机生成内容的最终问题便是我们很难生成真正有趣的内容。一个典型的例子便是MMO;《魔兽世界》中有无数地牢,每个地牢都需要玩家花费好几个小时的时间进行探险,并需要好几周的时间才可能精通。然而,一旦玩家精通了一个地牢,它们便不再具有多少变量和重玩价值。在《Anarchy Online》中很少有精心设计的地牢:然而,玩家可以进入随机的地牢中。从理论上来看,玩家不会遇到两个完全相同的地牢:然而在实际上,每个地牢给玩家的感觉都差不多。因为地牢是随机的,它们不具有叙述结构或整体的设计理念。比起独特感,每个地牢反而让人觉得都一样。

Spelunky(from xbox)

Spelunky(from xbox)

这些内容可以归结为多少规则正在落实行动以及生成是如何执行的:《Nethack》和《洞穴探险》都使用了随机生成关卡,并拥有强大的用户基础。然而有趣的地图设计的生成规则与随机生成游戏元素是不同的两个问题;我们可以说优秀的设计师应该清楚生成地图的局限性。我们仍然能够在这些地图的生成中使用这些随机讨论内容。

更认真的解决方法

有时候我们会将随机性彻底带离游戏。在RPG游戏中,比起将boss的出现几率设定为1%,我们可以选择让他们出现在玩家进行了100次杀戮后。在纸牌收集游戏中,对抗系统通过将每个可游戏的纸牌设定为土地而不是咒语去解决玩家需要土地的问题;这意味着你永远都不会觉得自己“受困”,并同时能够维持土地所呈现的势头。

然而,彻底删除随机性感觉就像不分精华糟粕全盘将其否定掉一样。在RPG中,让boss在玩家进行100次杀戮后再出现就等于将boss变成是可预测的对象,而当游戏太容易预测时,它就变成了一个谜题。所以更好的选择应该是让boss出现在每50至150次的杀戮后。这意味着boss的出现仍然是基于一个随机的范围内,但这种随机性却不再那么明显。

使用精心控制的数字也是一种伪随机的生成方式。存在许多方法能够做到这点:在《俄罗斯方块》中,如果我们生成了一个L砖块,那么对于接下来的三个砖块我们将“再次滚动筛子”以防止再次出现L砖块。通常情况下,L砖块的出现几率是1/7次,但再次滚动筛子将使它在这三个回合中的出现几率变成1/49。虽然它仍有出现的可能性,但这种可能性被大大缩减了。

当然了,这并非最佳方法:有很多方法能够随机生成数字,从简单的重新掷筛子到加重随机数字;在像《俄罗斯方块》这样的游戏中,留下1/7的机会去生成任何砖块可能会是最佳选择。

如果我们使用了随机性,我们同样也有机会引进seed。这意味着我们在游戏中使用的随机数字并不是真正随机的:“随机”序列是由名为seed的数字所定义的。在《俄罗斯方块》中,它出现在于我们不能预测会掉下怎样的砖块;然而,如果我们赋予《俄罗斯方块》游戏42这个数字,我们便会从方块,L砖块,T砖块,方块开始,然而每一次使用42 seed的游戏都将从这些砖块开始。尽管seed并未经常出现在游戏中(像《我的世界》和《FreeCell》便是两个显著的例子),但却是一个很棒的添加内容。

seed随机性的能力是源自计算机不能真正生成随机数字的事实:通常情况下,它们只能使用一个基数,然后通过计算去获得一个“随机”数字。通过确保基数每次都是相同的,计算将提供给我们同样的“随机”数字。

而关于删除随机性的选择便是为了更多地利用它。这在一开始可能看起来很疯狂,但却是非常有效的:滚动两个筛子,然后将其所得数字加起来,你便拥有1/7的几率去获得7。在这种情况下,通过使用这些随机性几乎就能够彻底删除随机性了。

结论

最后,我想说的是随机性并不是什么邪恶的内容;它归结起来就是一种感知。玩家想要接受挑战,或者拥有有趣的体验,但是丢给玩家一群随机的怪物既没有挑战也不有趣,且与他们是死是活也没多大关系。不管通过缓解随机性,我们可以创造出想要的结果,并创造出能够与玩家互动而不是忽视他们的游戏。

关于随机性的最后几点注意事项

随机性通常是解决冲突的一种糟糕方式,因为它会创造出不可预知的结果。随机性也是生成内容的一种糟糕方式,但它通常也是这么做的唯一明智的方式;就像设计一款《俄罗斯方块》般的游戏,我们就需要拥有一列关于每个砖块的掉落的列表。

当随机性出现时,它通常都是为了迎合玩家的洗好。如果游戏元素是随机生成的,它们将允许玩家能够对最糟糕的情况作出反应,但同时仍提供给他们合理的成功几率。

然而,我们必须接受有时候随机性也是必要的这一事实。如果没有随机性去决定战斗的话,RPG也就不再那么有趣了。特别是桌面游戏更不可能快速解决问题:对于蛇梯棋这类型游戏来说,丢1000次筛子并得出平均数是不现实的。

最后我必须强调的是:如果你彻底删除了随机性,那么你创造出来的可能就不是一款游戏,而是一个谜题。

篇目6,分析游戏设计中随机化的利与弊

作者:Josh Bycer

在游戏设计中,我最喜欢的词汇便是随机化。如果能够正确使用,它可以极大地提升游戏的再玩性。诸如《幽浮》和《暗黑破坏神2》之类的经典游戏都绝妙地运用随机化来让玩家不断玩游戏。近期出现的独立游戏,比如《Dungeons of Dredmor》、《Din’s Curse》和《Space Pirates And Zombies》,这些都是可以用来阐述随机化利弊的绝妙例证。

在我们谈论随机化的利弊之前,先分清可以在游戏中执行的随机化程度是很重要的。程度并非以个人偏好来划分,而是设计师使用随机化的方法。

低:装备放置位置和找到它们的可能性。动作RPG游戏通常使用这种程度的随机化。应当注意的是,你可以将重要道具设置在固定的地点,将普通道具和装备随机放置。

中:在低程度的基础上加上敌人位置的随机化。执行这个程度的随机化有两种方法,第一种方法是在游戏中设计“特别”的敌人。在《暗黑破坏神2》中,玩家偶尔会碰到有名字的怪物,这些敌人与其同伴看起来不同,而且名字前有特别的修饰词,比如强攻和火抗等。第二种方法便是随机安放敌人的位置。

高:包含以上两种程度,再加上世界的随机化。rogue-like游戏通常使用这个程度的随机化,但是这个类别并不局限于rogue-like题材游戏。《Space Pirates And Zombies》在游戏开始时允许玩家创造随机的宇宙。诸如《文明》之类的TBS游戏也允许玩家在随机化的世界或预设的地图条件中玩游戏。

现在,让我们谈谈使用随机化的优势所在。首先,随机化是让游戏产生再玩性的绝妙方法。你永远都不知道财宝箱中有什么,下扇门背后会出现什么,这将成为让玩家持续玩游戏的绝佳驱动力。游戏中随机化的元素越多,体验的新鲜感持续时间就越久。

当我同好友谈论起rogue-like游戏时,他认为此类游戏就像是赌博机。从某种程度上来说他的比喻是正确的,你永远都不知道自己是否会有好运获得所有需要的装备,情况也可能对你不利。

随机化还可以用来当作难度调节器,让游戏对新玩家而言是个较小较容易的世界,而对专家级玩家来说可能是个更大且更具挑战性的世界。《Din’s Curse》在这方面做得很好,玩家可以选择敌人的等级、世界的大小等要素。

当然,随机化也存在某些弊端。回到之前那个赌博机的比喻,虽然其奖励充满诱惑力,但是如果失败次数过多会让人们感到很沮丧。在游戏《Dungeons of Dredmor》中玩最高的难度时,因为敌人和装备出现的位置,我有90%的情况在跳下第一层楼之前就失败了。

Dungeons of Dredmor(from downtr.org)

Dungeons of Dredmor(from downtr.org)

随机化根据其程度深浅,很容易产生让玩家感到别扭的地图。回到《Din’s Curse》这款游戏中,我在游戏中多次碰到地下城入口聚集大量敌人的情况,或者在第一层便遇到名字上有着最强修饰语的BOSS。

其次,构建随机化关卡有可能影响游戏的质量。使用功能和基本资产创造随机化关卡很简单,但是创造出有绝妙视觉效果而且构建精巧的关卡就不那么容易了。

《Demon’s Souls》便是绝佳的例证,虽然你可以看到随机化关卡对游戏大有帮助,但是也必须承认其关卡是精心设计的结果。每个关卡都有着独特的挑战和风格,足见设计师花费了大量的精力来设计关卡。

我要提的首个随机化关卡的不良例证便是《Phantasy Star Online》。在这款游戏中,每个地下城的布局都是随机的,但是游戏中的每个世界都只有几个空间。这意味着每层或许有4个不同的空间模型,而这就是游戏的全部。关卡设计完全是自毁式的,让人感觉像是将各种元素拼凑起来。这也是我们在创造随机化关卡时需要避免的事情,游戏世界的设置需要某种程度的凝聚力。《Dungeons of Dredmor》的首款零售版本有展示房间样式的地图,这从某种程度上降低了游戏的质量。

第二个问题在于,你需要考虑的游戏机制越多,创造出像样的系统就越难。原因在于,玩家接触到的机制越多,需要编程和执行到引擎中的变量也就越多。在《Mine Craft》中,每次新游戏时都会产生随机化的世界,而这种做法之所以能够产生效果,其原因在与玩家只能通过方块与游戏进行互动。

假设有人为《杀出重围:人类革命》设计随机化关卡,要让关卡能够得以运转,引擎必须能够创造出随机化场景,允许玩法风格的改变,这就意味着关卡中必须有可以打破的墙体以及可以到达的区域。如果关卡中没有这些元素或者不允许其呈现游戏进展,玩家会因这种无法自行构建的关卡而感到沮丧。

要创造出能够发挥作用的随机化系统,就必须将其构建在线性的层次之上。这意味着,对于每个随机化元素而言,都必须有某些担保的东西。比如在《Dungeons of Dredmor》中,虽然游戏世界每次都具有随机化特点,但是每层的敌人样式基本都受到限制。你永远都不会在第1层碰到本该在第5层看到的敌人,反之亦然。

《Din’s Curse》的设计同样也是如此,每层中都有条向上的路和传回城镇的入口。就敌人而言,以玩家在生成世界时选择的初始关卡为基础,敌人会随着玩家在地下城中的探索逐渐变强。

再来看看《Mine Craft》,虽然世界的创造是完全随机的,但是每个新世界中都有着相同的基本规则:更深的地下能发现更好的资源,敌人在黑夜里生成,玩家可以自由前往所有的地方。有了这三个常量,玩家仍然可以在随机化的范围内体验每次游戏带来的新鲜感。

我希望能够在随机化的世界中体验游戏,无论玩家身处何处,游戏世界都应该围绕这个位置来构建。较难的地方离玩家较远,较容易的地方离玩家较近。大部分的建筑都是随机化的,但某些类似于地下城的特殊建筑则采用线性设计。这会让玩家获得不同的游戏体验,但是仍然有其中获得进步的感觉。

设计精巧的随机化系统可以提升你的游戏设计,给玩家增添额外的价值。但是,就像所有优秀机制一样,只有正确的设计和执行方式才能诞生理想的随机化效果。

篇目1篇目2篇目3篇目4篇目5篇目6(本文由游戏邦编译,转载请注明来源,或咨询微信zhengjintiao)

Games of chance: what does randomness bring to videogames?

By Edge Staff

In the autumn of 1975, Reginald ‘Rusty’ Rutherford watched a monster – his monster – wander around a computer screen at random. The orange glow of the vector monitor displayed a map, a tiny hero with a sword, and contextual information rendered in the solemn style of Dungeons & Dragons. This is Pedit5, the earliest known roleplaying game on a computer.

Rutherford was a programmer working on the University Of Illinois’ Programmed Logic for Automated Teaching Operations (PLATO) computer system. The hardware, built in the ’60s, was among the first networked computer systems to be used for educational purposes. In the ’70s, decades before the Internet came into being, PLATO could connect to around 150 locations worldwide, and it was regularly being expanded to new ones. Space on the system was limited, but Rutherford’s group had access to two unused files, which were labelled ‘Pedit4’ and ‘Pedit5’.

Disregarding the rules against such things, Rutherford took Pedit5 and began working to develop a game based loosely on Dungeons & Dragons. Pedit4, meanwhile, became an instruction manual for his new game. Rutherford was attempting to emulate an incredibly rich and intricate boardgame, but his program lacked complexity. Each dungeon contained one floor and about 50 static rooms. The creator puzzled over how to keep the experience from getting stale.

He found the solution within D&D’s rulebook: randomisation. If Rutherford allowed PLATO to make its own decisions about where to place the monsters and treasure, the number of potential level layouts would skyrocket, and the game had a much better chance of holding players’ interest. Rutherford worked on Pedit5 until late ’75, but soon after moved on, leaving behind his job, the PLATO system, and the two secret files that would later become the foundation of an entire videogame genre.

The idea to incorporate randomness into Pedit5’s design brought an age-old question into the computer age: what is possible when we let go and leave things to chance? But this is a question games have been posing in various forms for centuries, with human beings throwing their fates into the hands of random mechanics. Dice-heavy 20th-century race boardgames such as Ludo and Parcheesi can trace their roots back to the 1914 German-designed Mensch ?rgere Dich Nicht. The title translates roughly to “Don’t be mad, man”, indicative of the game’s frustrating level of randomness. An irritating dependency on luck would also define the 1965 family game night atrocity called Trouble or Frustration!, depending on which side of the Atlantic you lived on. In it, players have to roll a six just to get on the board and play. These games fall into the jurisdiction of the so-called ‘cross and circle’ family, which most researchers believe originated with the Indian game of Pachisi.

In his 1892 book, Games Ancient And Oriental And How To Play Them, English architect and writer Edward Falkener traced Pachisi back to the 16th century, where royalty reportedly played on enormous boards with live human figures as pieces. “[Indian Emperor Akbar I] and his courtiers played this game; 16 young slaves from the harem wearing the players’ colours represented the pieces,” Falkener wrote, “and moved to the squares according to the throw of the dice.” According to Falkener, the game was played by throwing six shells from cowries, a type of marine mollusc, in lieu of dice. Players would then count how many shells landed open side up. And Pachisi is predated as the first game to leave an element of play up to random rolls of the dice (or a fishy equivalent) by The Royal Game Of Ur – discovered in a Sumerian tomb in the 1920s – which dates to 2600 BC and featured tetrahedral dice.

All these games, from The Royal Game Of Ur to Trouble, are really just more codified versions of a favourite BC pastime: casting lots. These were games of chance – simple systems built around a simple luck-based tool. Randomness has always been the easiest way to leave the outcome of a competition to the whim of God, instead of solely to skill.

But while today’s games – both board-based and digital – owe a debt to the history of early randomisation tools, it’s only in recent decades that we have begun to discover what randomness can achieve when we harness it in new ways.

‘Create New World’ says Minecraft’s rectangular button. Click it and an instant later you might find your nose pressed against a vine-covered tree trunk. Or perhaps you’re standing knee-deep in water. Maybe you’ll appear in a pumpkin patch, with a spotted cat lurking nearby. The possible scenarios are so numerous as to be effectively endless, generated by a fixed algorithm in combination with a random or player-given ‘seed’ sequence. Since each seed grows into a different world, few buttons can surprise and delight like the one in Mojang’s blockbuster hit.

Minecraft is about creating, living, and working resourcefully within a universe that has been instanced just for you. As a result, no GameFAQs walkthrough can tell players what’s around the corner in any particular cave, and YouTube tutorials won’t show you exactly where to find diamond ore. Since the advent of the Internet, videogames have been, as a rule, unable to hide secrets from their players. With its randomness, Minecraft spurns becoming solvable. You can learn how the game works at a basic level in minutes, but you have to play well to survive and thrive.

Like boardgames, videogames with lots of design randomness have traditionally been abstract. Think of Tetris with its ‘T’ and ‘L’ shapes, and that ever-elusive long block. Or consider Bejeweled, a game of raw systems built on the concept of matching like-coloured objects that fall from the heavens. But neither is truly random. Tetris, for example, generates randomly shuffled but discrete sets of all of its block types to ensure you never play a game that presents an endless procession of ‘Z’ blocks.

But games need not be abstract to benefit from leaving swathes of the experience up to chance. The Diablo series has progressively striven to randomise as much of its own content as possible, from loot drops to map layouts. And 2009’s Borderlands, with its purported 17.75 million guns, used a random item generation system as the crutch of its marketing campaign. Even popular mass-market series like Gears Of War are embracing randomness to alleviate repetition. Die and restart a level in Gears Of War: Judgment and its Smart Spawn system sends you a group of new, randomly selected enemies to fight.

The notion of semi-authored randomness is core to the genius of Spelunky, Derek Yu’s sublime merging of the platforming and roguelike genres. Like many games before it, Spelunky features randomly generated levels, in this case constructed from rooms made of randomly selected tiles from a fixed set. An algorithm then runs a set of checks and populates the level with monsters and obstacles, which are also subject to randomness, but balanced by intelligent rules. You play this setup just once. Either it kills you and you restart, or you succeed and move on to the game’s next world, which is built from a new set of level tiles and algorithms.

One of the most obvious benefits of this is that the game avoids becoming repetitive. Instead of falling back into a world you’ve partially conquered once, Spelunky’s levels ask you to overcome a new space that’s forged by the same consistent laws. It expects you to navigate the new landscape using what you’ve learned from previous runs.

Randomness allows players – and even creators – of games to be continually surprised by them. But is randomness in game design all about world and weapon generation? Is creating a game with endless content a goal worth pursuing? Yu doesn’t think so.

Needlessly padding out a game’s length using randomness, he says, can be “one of the worst things you can do unless you’re still introducing new things to the player and giving them a great experience”. To Yu, this means constantly observing and learning new things, being challenged, and “having your concept of the world expand”.

An extreme example of a not-fun implementation of a random system would be a slot machine. Pulling a lever and watching cherries and lemons spin teaches you nothing – the spinning is meaningless noise. Trying to understand and control it is a little like trying to divine a pattern in TV static, but that doesn’t prevent some people from becoming hopelessly addicted to the machines due to the rush of endorphins they can induce. Yu calls this handling of a system by the human brain “the inherent addictiveness of randomness”.

The human brain is wired to find patterns in noise, and so even when an implementation of randomness is just static, players will often interpret it as coming from an unseen controller.

Take Valve’s 2007 hit Left 4 Dead, which may have fixed, unchanging levels, but employs great degrees of randomness in other areas of its design. Vital resources such as health packs and weapons can be found in different quantities and locations on each playthrough. The game’s hordes of zombies are also placed at random, with huge waves bursting forth at times, all dictated by an underlying AI system called The Director.

The Director is really just a name given to a collection of algorithms that dictate the implementation of Left 4 Dead’s random elements. It has no emotions nor goals of its own, but players have latched on to the idea of The Director as an entity, and often discuss the game in terms of them versus it.

In a YouTube video titled The Director Hates Us, one player describes a recent gameplay experience. He says that The Director chose to attack him and his friends, and hid vital health pickups from them until a late part of the level. “He also threw a Witch right next to where the news van and minigun were,” he wrote in the video’s description.

Of course, the man behind the curtain is just a machine, but because the verdicts and outcomes delivered by it are unpredictable, players have anthropomorphised it. The Director – or the bits of code that compose him – assumes a living quality because unpredictability makes him seem human.

It is a basic human need to see agency and patterns even when there are none. It’s easy to imagine how that trait has remained an asset throughout the process of human evolution. Keith Burgun, designer of the game 100 Rogues and author of Game Design Theory: A New Philosophy For Understanding Games, argues that this inheritance reveals itself when people play games. When children play a boardgame like Candyland, he says, they believe that they have agency behind their dice roles. Even as people get older and begin to understand that they don’t have real control over things like dice, they continue to attribute successes in mostly random games to themselves.

“People stand up and celebrate when they roll a 20 in Dungeons & Dragons,” says Burgun. “They allow themselves to participate in this very human thing, this tendency to see agency where there isn’t any.” Burgun remains quiet for a moment, then observes in a manner and tone that is characteristic of him and his work: “This is also the origin of God.”

A few weeks after Rusty Rutherford left his job and his pioneering game behind him, the 18-year-old Paul Resch walked into the basement of a building on the University Of Illinois’ campus. Resch descended a flight of concrete stairs and entered a room lit by the neon-orange glow emitting from more than a dozen PLATO system displays. Groups of students huddled near the machines, using them to learn about the cross-breeding of fruit flies for a biology class. The program seemed to be some sort of educational game.

His own class wasn’t scheduled to begin yet, but Resch desperately wanted to toy with the highly advanced machines, so he pretended to be one of the biology students, stealing access to one of the terminals. It was here, through chance, that he discovered an abandoned file: Pedit5. Resch gathered some friends and over the course of the following months began modifying the code to improve it. Resch built networked multiplayer and a chat system into the game. He added more rules from the Dungeons & Dragons universe, and even retrieved proper permission to use them from Tactical Studies Rules (popularly known as TSR), D&D’s publisher at the time.

TSR’s response was mostly one of bewilderment. “They wrote back saying, ‘Sure, we don’t know what you’re talking about, but OK,’” Resch explains. More changes were made, and Resch decided that his game had evolved enough to warrant a new name. He titled his modified game Orthanc, naming it after Saurman’s tower from The Lord Of The Rings universe.

Eventually, Resch designed an algorithm that would automatically create random levels for Orthanc. Every six months in real time, the algorithm would run and Orthanc’s old world would disappear forever, replaced by a new, albeit temporary, one. A couple of weeks before the level-change event would occur, a message would display to PLATO users currently active in the game: “New levels are coming.” It was a friendly heads up, but also a warning – if users kept playing right up until the moment of the level-generation event, the entire world would vaporise around them, and a new one would materialise. It was more than likely that they’d then find themselves trapped by walls on all sides.

Resch – who is now 55 and has worked for companies including Atari, Apple, and Google – was developing features that we didn’t associate with videogames in the mid-’70s.

But why design a complex algorithm that only executes twice a year? Why not just design the levels yourself? Resch’s reasoning was both cogent and macabre: he knew he wouldn’t always be around to make new content. In a way, he was taking out an insurance policy in preparation for his own inevitable death.

Randomness in games has often been about replacing or simulating humans. Rutherford and Resch modified their games to become self-replenishing. The Smart Spawn system in Gears Of War: Judgment is a miniature designer included in the game who watches players and provides those who have to repeat a sequence with something new to play with.

Other videogames, such as Spelunky and Minecraft, make use of random systems not to pad out their length, but to allow for surprising situations to come about on their own. It’s a totally different application of randomness than when you roll a die and win based purely on luck in a game of Ludo or Parcheesi.

The surprise that comes when a game cooks up an amazing thing the designer didn’t think of is the sort of gift only videogames are capable of giving. So when choosing the random games we make or play, it seems wise to ask a simple question: do we want to be surprised, or do we simply want to feel lucky?

篇目2,Chance and Skill in Game Design

Lennart Nacke

Welcome to the fifth week of class in the course: Basic Introduction to Game Design. Make sure to read the syllabus and course information before you continue. Today, we are going to discuss chance and skill in game design. This text follows closely from our textbook (Challenges for Game Designers, Chapter 5 and 8). I also take inspiration from Schell’s The Art of Game Design (Chapter 10, pp.150-170) and Adams’s and Rollings’s Fundamentals of Game Design (Chapter 11). However, this is the part when I break free.

Games, which feature meaningful decisions, do not always have to require or evoke skills from a player. Some games operate entirely by chance. Games that rely more heavily on chance than on skill are often found in the context of children’s games or gambling. Why does this difference matter? The player is going to play, play, play, play, play – are they not? Do not shake off the notion of chance too swiftly. Games of chance can be very engaging, because they can allow players of different skill sets to engage in a balanced competition. Games are for everyone; for people, who are used to rolling the dice and people, who like to feel the fear in their enemy’s eyes. Some people even think it is fun to lose and to pretend. However, games of luck in particular seem to feature more attainable goals and are winnable by more people.

On the other hand, games like Tic-Tac-Toe are entirely skill-based and can be mastered, once a player figures out a dominant strategy. See this example lecture for forming a Tic-Tac-Toe strategy via reasoning.

It might seem crazy what I am about to say, but there are several reasons for games to use chance as a game mechanic:

The game designer wants to prevent or delay the player from solving the game.

The game designer wants the gameplay to be balanced and competitive for all different kinds of players.

Chance can increase the variety of elements in your game system.

Chance can help you create dramatic moments in your game.

Chance can enhance the decision-making in your game.

On Game Balance

Adams and Rollings describe a balanced game as “fair to the player or players, [...] neither too easy nor too hard, and makes the skill of the player the most important factor in determining his success.” A game that is considered well-balanced, therefore, has the following characteristics:

The game provides meaningful choices. Several strategies can allow the player to win. There is no dominant winning strategy in the game.

Chance does not play a role so great that player skill is irrelevant. A player with more skill should be more successful than a poor player.

The game’s level of difficulty should be consistent. The players perceive the challenges in the game as not abrupt and within a reasonable range of their abilities.

In Player-vs-player games, the following characteristics also apply:

The players perceive the game as fair.

Any player, who falls behind early in the game, gets some opportunity to catch up before the end of the game.

The game seldom or never results in a stalemate if the players are of unequal ability.

Playtesting for luck and skill balance

When balancing games, an important factor to consider is the balance of skill and luck elements in the games. Some of the following are signs indicating that your skill/luck balance might be off:

Your players are bored. This is generally a sign of missing interesting decisions in the game and too many luck elements.

Your players are only bored when it is not their turn. Your game is likely lacking some strategic elements as none of the things players do during their turn seem to affect other players’ turns.

Your players do not become engaged in the game and are confused about what to do. This could be a sign of too many decisions or too much information to process for players.

One of your players beats all the other players by a wide margin. This could be an indicator that your game is heavily skill-based and one player has mastered this skill. To keep a game balanced for players with different skill levels, it is important to add some elements of luck to it.

Generally, adding “luck” to a game comes down to adding elements of randomness. In board games, this is often done through dice rolls or shuffling cards. If you find that you are using too many of these random elements, you can replace them by using distinct automated advances (e.g., moving a player token a distinct number of spaces during a turn) or by adding a player decision instead of the random element (e.g., players can choose from a given range of movement options). Player decisions are not just complex thinking decisions at all times, but can also be split-second dexterity-based decisions (twitch skills like hitting notes in Guitar Hero).

Our textbook (Challenges for Game Designers) distinguishes between three types of luck/skill games:

1.Games of chance. This can be either children’s games or gambling games. These games can often be enhanced by adding twitch and strategic elements to them. Often just the illusion of skill in those games is enough to make them more interesting.

2.Games of twitch skill. These are games that are focused on a challenge of dexterity. These games tend not to work too well with chance elements, but adding simple tactical options is quite common. Anything that keeps the flow of the game is a possible addition.

3.Games of strategic skill. These games can feel tense and slow, because they involve a lot of thinking. Adding twitch elements can be a welcome interruption of these long strategic sessions. Many long-winded RPGs feature little twitch mini games (such as lockpicking in Skyrim) to interrupt some of the longer stretches.

Types of Skills

Jesse Schell distinguishes between three main categories of skill in his Art of Game Design book. Keep in mind that many games require a blend of different skills, but these categories provide a starting point:

1.Physical skills: Skills like dexterity, coordination, strength, and physical endurance. These types of skills are most commonly found in sports games. However, some might argue that the correct keypress and controller sequences found in some esports would also fall into this category.

2.Mental skills: Skills like observation, memory, and puzzle solving. Often these relate to making interesting decisions in a game, as most interesting decisions are also tactical decisions.

3.Social skills: Skills like reading an opponent, tricking an opponent, and coordinating with teammates. These relate to a player’s ability to make friends and influence people in a game. They are often tied to a player’s communication skills. This is also commonly seen in team-based sports.

Schell also distinguishes between real skill, which means your actual skill as a human person in controlling the game in a certain way, and virtual skill, which relates to your in-game character’s skill at doing something. Real skills only improve when you work on them, while virtual skills can improve even when your real skill does not improve. In general, Schell suggests making a list of all skills in your game as an exercise to break down your game into skill components. Finding out what skills you require from your players will make you a better designer.

Chance

Chance can make games more fun, because it adds elements of uncertainty to it. Uncertainty equal surprises for players and humans do enjoy surprises. Chance is also related directly to probability in games, and Schell lists ten rules of probability with which game designers should be familiar:

1.Fractions are decimals are percents. Fractions, decimals, and percents essentially all work the same way and are essentially the same thing: 1/2 = 0.5 = 50%. As humans, we like to express probabilities in percentages.

2.Zero to one – and that’s it. This concerns, of course, probabilities, which all happen in the space between 0 and 1 (i.e., 100%). Chances like -10% or 110% do not exist when we speak about probabilities in games. If you are trying to calculate the probabilities of your dice rolls and they come up higher than 100, you know that you will need to run your calculation again.

3.”Looked for” divided by “possible outcomes” equals probability. Probability really means you take the number of times the outcome that you are looking for can (or has) come up and divide this by the number of possible outcomes (in the case that all outcomes are similarly likely) .

4.Enumerate. Let’s say that you are trying to find the outcomes that you are looking for and it is not as straightforward as the numbers on a D6; a good way of getting to your answer is just to list all the possible outcomes in your scenario. This helps you see patterns and combinations.

5.In certain cases, OR means ADD. When trying to determine the chances of x or y happening (like drawing certain cards from a deck) and these events are mutually exclusive, you can add the probabilities to get the overall probability of an OR event.

6.In certain cases, AND means MULTIPLY. When we are looking for the probability of two things happening simultaneously, we can multiply their probabilities. This only works if the two events are NOT mutually exclusive.

7.One minus “does” equals “doesn’t.” This quite logical as 1 represents a 100% chance of something happening. So, whenever you have calculated the probability of something occurring, you can subtract this number from 1 to find the probability of the opposite event occurring.

8.The sum of multiple linear random selections is NOT a linear random selection. By linear random selection, we are referring to a random event where all the outcomes have an equal chance of occurring. A die roll is a great example of this. Adding multiple die rolls does not mean that the possible outcomes have an equal chance of occurring. Rolling a die twice means that you have a higher chance of a seven occurring. The possible outcomes of this scenario follow a probability distribution curve (a normal distribution in this case), where the numbers in the middle (6,7,8) have a higher likelihood of coming up.

9.Roll the die. Schell distinguishes between theoretical probability and practical probability. Theoretical probability is what we have talked about so far. It is what is likely to happen in a general case. However, practical probability accounts for what has already happened. For this you would just roll a die over and over and record the number that you are getting and calculate your probability based on this. Ideally, this probability should approach the theoretical probability with a repeated number of trials. This is also known as the Monte Carlo method.

10.Geeks love showing off (Gombauld’s law). Schell basically recommends to find a math wiz friend, whenever you are facing a probability problem that you cannot solve on your own. This can also include posting math and probability related questions to mailing lists.

Some important things to remember about chance are (from Adams and Rollings):

Use chance sparingly.

Use chance in frequent challenges with small risks and rewards.

Allow the player to choose actions to use the odds to their advantage.

Allow the player to decide how much to risk.

篇目3,Randomness and Game Design

by Keith Burgun

For thousands of years, we’ve relied on randomness of various kinds to help our interactive systems work. While there will always be a place for randomness of all sorts in some kinds of interactive systems, I believe the current assumptions with regard to randomness in strategy games are largely wrong.

The major point I’d like to make is that noise injected between a player’s choice and the result (here referred to as output randomness) does not belong in a strategy game.

What is “randomness”?

For the purposes of this article, randomness refers to “information that enters the game state which is not supposed to ever be predictable.” The process by which random information is generated is designed to be something that humans can never figure out. Classic examples of random systems are rolling dice, shuffling cards, or random number generators.

Technically speaking, a die’s rolling pattern is not actually “random”. It’s simply responding to physics, and a computer could take information about how a die was thrown and predict the number that would come up. We use dice precisely because a human being can’t do that. In fact, when we incorporate dice into our game designs, we do it under the assumption that no human will ever be able – nor likely even try – to predict the outcome.

In fact, trying to actually predict how the die will roll, by perhaps carefully tossing it with a specific, intended trajectory, so that it rolls to a side you intend, would likely be called out as “cheating” by any observers. The whole idea with a die is that you’re not supposed to know. It is noise that must remain noise, forever.

Part of the reason for this is the fact that we’re actually dealing with two separate, closed systems in a game that contains randomness. A rolling die is a closed system of its own that really has nothing to do with the greater game system.

This is distinct from other kinds of “unpredictable” or “uncertain” events. In chess, for example, players have some limit to the number of turns they can look ahead. Beyond that point, the events that occur are indeed unpredictable for that player. However, players can and do learn to look further and further down the possibility tree as they get better at the game. Part of the skill of chess is being able to explore that ever-increasing possibility space and come out with more predictive ability.

So while chess does have unpredictability, it does not have randomness. All games must have some kind of unpredictability in order to function, but randomness isn’t the only way to achieve that. Chess’s source of unpredictability – a highly complex game state – is unlike a random source in that it can slowly be chipped away at and understood.

Types of Randomness

Randomness can be separated into two categories: input randomness, and output randomness.

Output randomness – when we think of randomness in games, we’re usually referring to this. Output randomness is noise injected between the player’s decision and the outcome. Examples would be the dice roll combat in Risk or Memoir ’44, or the random number generation combat in X-Com or FTL. I will refer to systems that do not have this type of randomness as “deterministic”.

Input randomness – this type of randomness informs the player before he makes his decision. Typical examples of input randomness would be map generation in Civilization or Rogue, or face-up tiles or cards in a worker placement game like Puerto Rico or Agricola. (People often use the term “procedural generation” to refer to this kind of randomness in digital games.) This article will not focus on this type of randomness, but it’s important to know the distinction.

Interestingly, while these two types are certainly distinct enough from each other to warrant the classifications, they do technically exist on a continuum. Without going into much detail on it, it should be noted that irresponsible use of input randomness – where the player has very little time to respond to the new information, or where the game generates problems of wildly varying difficulty match to match – cause similar problems as output randomness.

The Strategy Game Learning Engine

Strategy games are engines that allow us to understand them. We play a game, we win or lose, and we make connections. “Oh, I see!” we say as we figure out some element of how the system works. For evolutionary reasons, we find this process enriching and entertaining. This is the “essential fun” of strategy games (largely the premise of Raph Koster’s book, A Theory of Fun for Game Design).

Let’s break down the process further.

Informing the Player – The player takes a look at the game state, trying to figure out what move to make. He is informed by his “skill” database – the collective total observations about the system and how it works that he’s made up until now.

Deciding the Move – A move is chosen, and the action is taken. As a result, the game state is changed. Alternatively, this could be “deciding the strategy” – a series of moves that collectively adds up to a larger strategic gambit.

Feedback in Outcome – Over the course of the rest of the game, the system responds to this input. A series of events take place after that decision, including the final win/loss event; all of which serve as feedback for the player, highlighting some causal relationship between them. Feedback also comes following a strategy, or at the end of a game.

Recording Skill – The player observes and records this cause-effect relationship and records it to his database. The player can then use that skill to make moves in the future. (Notably, this moment is where the essential “fun” of strategy games comes from, but it of course relies on the rest of the machine to function.)

As a player plays a game, over many matches, he builds to this “skill folder” and becomes a stronger player. In a shallow game, there might not be very many of these moments, whereas a very deep game can continue delivering these moments for decades if not lifetimes. This is generally why it’s considered a good quality for games to be strategically “deep”.

How do we achieve that depth? Well, the first way, which all game designers already understand, is emergent complexity. In order to create complexity, we design our games so that they generate complex emerging situations throughout play. A bishop, knight and rook against three pawns and a queen is not inherently complex; there’s a very small amount of data there. However, unleash these two forces on each other on a chessboard, and the amount of possible situations that could emerge is huge.

Complexity Effectiveness

The second method for achieving depth is, as far as I can tell, not understood by most designers today. This method involves being aware of complexity effectiveness: the amount of correlation between a state, and the history of past states.

A strategy game only has a finite number of states throughout a match. From what I can find, it seems that the average number of moves in a chess game is somewhere around 40, for example. A real-time game doesn’t have discrete “turns” per se, but there’s still a finite number of meaningful states, no matter how you divide it up.

If your game is a continuous series of events that lead causally from one to the other, then you are maximizing the amount of unique situations that can occur. I think this idea is counter-intuitive to many, who think that random events occurring somewhere in there must increase the amount of unique situations. However, the opposite is actually the case.

Having a system be entirely deterministic causes your emergent complexity to be maximally effective. This is because each emerging situation is given the maximum amount of contextual nuance by all of the events that came before and after it.

The pink egg here represents the current game state. In the deterministic game, it is getting pulled at by the events along the timeline of the match. In the random game, the timeline is severed and the current gamestate isn’t affected by as much.

In the deterministic game, the current game state has ties to every part of the entire timeline. Because of that, it is being pulled into a more complex and more unique shape. What this is illustrating is the way that context, when causally related, provides meaning to a game state.

Of course, even highly random games do have some deterministic elements that do provide some context to game states. For instance, in a game like Summoner Wars (a turn-based wargame involving dice roll combat), the health of your summoner and the positions of units are both relatively deterministic and do provide some context for game states.

However, the vast majority of contextual information in a game no longer has meaning. I attacked your unit, and I rolled the dice. It came up as a “miss”, and then next turn you killed that unit. That event – you killing my unit – is not really causally linked anymore to the actions I took beforehand. What happened was that I took an action, then something random happened, and then you took an action. The tie has been severed, and we can no longer use my move as contextual nuance for our current game state. Your game is now no longer “A, therefore B, therefore C”. Instead, it is now “A, then B, then C”.

The most significant bit of feedback is the goal-state. Once a match has ended, that win/loss condition sends a charge backwards through the course of events, revealing a positive or negative charge for every event that led to it. This move was somewhat good because it led to this, which led to that, which led to this, which led to that, which led to my win.

This is not to say that when a player wins, all his moves were good moves. However, it does provide an anchor point that informs every other move. Of course, moves are made in an attempt to get the player as close as possible to the win state. Once the match ends, we can now see how and why each of those moves was effective. (Because of this, players can get a lot of the same kind of fun out of watching a replay and analyzing it as they can from playing the game.)

Overall, after playing a deterministic game, a player is left looking at a coherent strategic picture that has been painted over the axis of time. Alternatively, the non-deterministic game could perhaps be considered more like a number of incomplete pictures. In this way, the deterministic game maximizes its complexity effectiveness, and the non-deterministic game does not. The non-deterministic game is adding complexity, whereas the deterministic game is multiplying it.

Imagined Depth

Output randomness does not increase the depth of a game. How could it? There is nothing to explore in a dice-roll. We all know that the odds are 1/6 for any face coming up. There is literally nothing else to know or explore.

What it actually does is obscure the outcome. You may have played perfectly, and still lost. The game has now sent you off on a wild goose chase, thinking about where you must have messed up, when in fact your play wasn’t the problem; dice rolls were.

Because of that wild goose chase, the game seems more complex than it is. The game provides unreliable feedback, and only after playing many, many games will it become clear which feedback you should ignore. Essentially, random games delay learning – the essential fun part of games – by injecting false signals into the engine. It’s a super-cheap way to create the appearance of depth, which is why it’s incredibly tempting for game designers.

Humans are pattern-seeking animals. We see figures in the clouds, we see images in the static, and we see conspiracy where there’s only coincidence. The reason is due to the fact that it’s evolutionarily favorable to think this way. The same quality that causes a person to think he saw a ghost in some rustling bushes is the quality that causes a person to think he saw a lion in some rustling bushes. And over time, those who thought they saw a lion were the ones who escaped when there actually was a lion. Those were the people who passed their genes along to us.

For this reason and others, we’re now both cursed and blessed with seeing patterns everywhere we look, and game designers have been exploiting this in us for as long as games have existed.

Gambling machines have always relied on psychological tricks to exploit us into playing them. In order for anyone to actually want to play something as vapid as slots or roulette, some degree of self-deception has to take place. On some level, the player has to feel like he is responsible if he wins. Otherwise, how can they be invested at all? From ancient religious superstition (the Gods are angry at me!) to their more modern counterparts, like “blowing on the dice”, kissing “lucky” items, or other self-deceptions such as the gambler’s fallacy, we find ways to attribute meaning to events that are actually pure noise.

Serious players of highly random strategy games tend to be skeptical that this same trick could be working on them when they play their Summoner Wars and their Hearthstones. But why? If players are able to perform this trick on themselves in a system that has no strategy at all, it seems very easy to believe that such tricks would work on a smaller percentage of the overall system. In fact, baking random elements into a strategy game makes it all the easier to conflate noise and strategy feedback, because some of what happens in the game really is strategic and deterministic!

In these games, there is the actual skill of the game, but then there is also an additional “phantom skill” amount, which makes the game seem vastly deeper than it is. In actuality, most players probably have the system close to solved somewhat quickly, and the randomness is the deciding factor.

Counter-Arguments

I’ve been arguing this position for a few years now, and over time I’ve encountered a number of counter arguments that I’d like to address.

“Output randomness is just input randomness for the next turn.” – Game designer and blogger DanC of the Lost Garden has said this to me numerous times in response to my positions. Basically he’s arguing that there is no actual difference between output randomness and input randomness.

This position has two major flaws. One is that it seems unaware of the possibility of a larger strategic picture that could be providing tons of complexity effectiveness that otherwise you’re losing out on.

The other major flaw is that even if it’s actually input randomness for the next turn, that’s what I call “unfair input randomness”. It’s up so close in your face that you don’t have time to respond to it. You now have a significantly different game state than you did a second ago, and there’s no discernible reason for it. On some games, you might play optimally, but get put into this position and lose anyway. On other games, you don’t get put into that position because the dice rolls go your way. Input randomness, when put up close enough to the player so that he can’t plan around it, is basically output randomness. Feedback is being artificially delayed.

Ironically, I agree with Dan’s sentiment that there’s no significant difference between output randomness and input-randomness-for-the-next-turn, although I think they’re equally bad.

To really drive the point home, imagine a scenario where you have a character who has a “to-hit” dice roll against a tough monster. He swings, and he misses! Well, that’s ok, it’s just input randomness for the next turn, after all! He tries to attack again, and misses again! At this point, you may already have lost, and it wasn’t because of any decision you made.

“Some games need output randomness to work.”

If you were to just rip the dice rolls out of Risk, it definitely wouldn’t work.

This simply means that they are shallow games. It’s understandable, because creating a coherent system that is deep is very, very hard to do. However, this is not a defense of randomness; more an indication of a weak design.

“If there’s randomness, then it’s all about risk management.”

A favorite of poker players. The idea behind this argument is that having random elements adds a “factoring in your odds” element to the game. You have to weigh the odds of outcome A happening against the odds of outcome B against the benefit of outcome A and the benefit of outcome B, and that makes games more interesting. Essentially, it’s combining odds and valuation.

This kind of risk management is not unique to random games. In any game that you haven’t solved, really every move you make is to some degree a risk that you must manage. In chess, there could be two major strategies – strategy A and strategy B. You might figure that A is more likely to work than B, but B has a bigger payoff than A, for instance. Randomness isn’t necessary.

As to the “calculating odds” aspect of this, determining odds is never interesting, especially not when you’re talking about something like counting cards in poker. Calculating odds in a deterministic system might be harder to do, but it would certainly be far more interesting due to all of the variables at play in a good, dynamic strategy game.

“Randomness doesn’t matter – just do the best you can!”

The argument goes something like, “if you care about randomness, you care too much about winning. Just have fun!”

This argument is not actually a defense of randomness in strategy games; rather, it is a defense of randomness in toys. Strategy games have a win/loss condition. If you are telling us to ignore that in FTL, then you are saying that FTL is a toy and that’s why randomness is OK.

“Players with a wider skill range can compete against each other.”

If a grandmaster and a newbie play chess against each other, the result won’t be interesting or fulfilling for either party. That much is true! This argument suggests that the answer to that is to throw in some randomness.

Of course, that’s throwing the baby out with the bathwater. You’ve now severely damaged your game for the sake of presenting people with the illusion of more-similar skill levels. The real answer to this problem is good matchmaking.

“Randomness makes a game more like real life.”

To quickly counter this argument, let’s simply assume that there is a set of values for strategy games which we can separate from the set of values for a simulator.

“Games with randomness still have skill to them!”

True, and I haven’t argued otherwise. The issue is that on a practical level, you will be able to actually explore less of that space in your lifetime, since so many of the games are essentially wasted on false random outcomes.

Other Feedback Distortions

I should also note a few types of output randomness that are not usually regarded as such, but function so similarly that they have many or all of the same pitfalls.

Simultaneous Action – Trying to guess what the opponent will do in RPS, for example, is effectively random. In fact, that’s why we use it to decide who has to go take out the trash – we consider it fair, because it’s random. The whole reason people agree to use RPS as the determining factor for who will take out the trash is because they know that there is nothing that they or their opponent can do to increase their chances. (Sure, there’s some study that says people are slightly more likely to play rock. But did your opponent read that study, or not? You’re now back to square one.)

Execution – Execution in games is a matter of “can”, not “should”. Can you press this sequence of buttons before my jump kick hits you in the face? Execution is still slightly better than randomness probably, due to the fact that you can at least get better at it. However, inside of a single match, it’s basically the exact same thing. The complex chemicals, nerves, muscles and tissues that stand between “what you wanted to do” and “whether your body actually makes the desired input” has tons of room for error. When you choose to make the input for your Dragon Punch, will it actually work? It’s effectively random.

Conclusion

Our collective perspective on randomness in game design really hasn’t budged much in 4,000 years. It’s time that we really gave this question some serious thought.

I’m not arguing that there is no place for any kind of randomness in game design. In fact, I argue strongly in favor of well-balanced, low-variance input randomness in multiplayer games. And single player games require input randomness.

However, output randomness in all its forms is to be avoided. The only time you should use randomness of that kind is if you’re making a gambling machine, or if you’re insecure about the depth of your system.

篇目4,Luck In Games

by Noel

The amount and type of luck involved in a game has a profound impact on the feel of that game. Some games have no luck whatsoever, and all the variation comes from what the opponent does (chess), some of them are all about luck with not much else (roulette), and most of them fall somewhere in between, creating a wide spectrum of possible experiences.

We don’t talk much about the role of luck in video games, probably because it’s hidden away under the black box of the computer simulation, but just like with board games, it can have have a large impact in the type of experience the video game provides.

Thinking about luck in these terms was crucial for the game I’m working on (still unannounced!). We made some crucial decisions thinking about how luck was part of the game and kind what kind of experience it created for the player. I’m hoping this post helps people with similar design challenges.

This post should apply to any kind of game in general (board or video games). Next time, I’ll be focusing especially on luck in video games using this as a launching point for a deeper look. Also, I’m limiting the definition of luck to random effects built into the game system itself, and not due just to player interaction.

No-Luck Games

In games with no luck, players rely completely on their skill to win. In that way, they’re closer to sports. Games become an intense, straight competition, pitting players’ brains against each other. Right there it shows how luck (or in this case, the absence of luck) creates a very specific feel to a game.

Good examples of games without any luck are classics such as Chess or Go. There are also plenty of modern board games with no luck, like Puerto Rico, Caylus (they both have a minimal amount of luck in the initial tile order), or Hive.

It’s interesting that a lot of abstract games tend to have no luck, and the more thematic a game gets, the more they seem to rely on luck.
Are You Feeling Lucky?

Having some amount of luck in a game can be very beneficial for most kinds of board games. It accomplishes many things:

Keeps things varied from game to game

Keeps players feeling they have a chance to win even if they’re not currently ahead

Removes pressure from winning players (“If someone beats me, it’s because they had a lucky streak”)

Makes players who didn’t win feel they stand a chance next time they play (“next time I’ll catch a break and I can win!”)

Points 2, 3, and 4 all encourage more people to play the game and feel they’re competitive at it, even if they didn’t win (and even if they’re not really competitive). One of the best examples of this is poker: Everybody feels they can do great at poker, if only they get good cards. In reality, this is not true in the long term, but poker introduces plenty of luck that it really is true in the short term.

A consequence of all those points is that having some amount of luck allows players of different skills to participate in the same game and enjoy it equally. For games that rely on having multiple people looking to play it, it can be a big factor.

Types of luck

For games that choose to add some luck element, there’s a whole range of amounts and types of lucks they can use for different effects. Unfortunately, it’s also possible to mix the wrong type of luck with a given game feature and create a frustrating experience instead of an enjoyable one.

Post-action luck. This is luck introduced after the player has made a decision and executed an action. It can be in the form of flipping a coin to see if you unlock a chest, or rolling a dice to see if your armies invade a territory.

Pre-action luck. Pre-action luck consists of the random events that happen before the player performs an action. The player is able to take them into account and make a decision based on them.

Hidden information. Hidden information is the third kind of luck. I was a bit hesitant to include it as its own category first, but it seemed different enough from the other two to warrant being listed on its own. Hidden information refers to things that are known only to some players and will affect other players or the game scoring.

Dice troyes

Post-action luck

OK, I’m going to say it: I’m not a fan of post-action luck. The player has already made its action and the outcome is random (even if it’s based on a probability curve the player is aware of, like rolling 3 six-sided dice). Since it doesn’t add to the choices the player has, it’s mostly uninteresting. This is the kind of luck that can add a bit of spice to an otherwise boring game, it doesn’t do much to make the game more interesting.

When used incorrectly, this kind of luck is extremely frustrating. The player can feel they chose the “best” action, but they rolled double 1s and their move backfired on them. Sure, there was some tension knowing that could happen, but was it really fun? Maybe the first time or two, but probably not long term.

While I typically really don’t like this kind of luck in my games, there are some situations in which even I will add it can add some interest to the game.

The first case is when the player can choose to perform one action or another, being aware of the different probability curves for both actions. For example, you can roll a single die and deal that damage to an enemy, or you can roll two dice, but if you roll two 1s, your character gets hit instead. In a situation like that, even though it’s still post-action luck, the player was presented with a meaningful decision ahead of time and had to weight the risks and rewards of both options.

The second case where post-action luck can work is when the action is repeated many times over the course of a game. That way, the outcome of each individual action in themselves is not game-breaking, and all the actions will eventually add up to the average over the course of the game. Luck in this case introduces a bit of noise and slight excitement without affecting things much.

This is a good situation to combine with the ability for players to slowly change their probability curves over the course of the game. That way, they can increase their chances of success for an action as the game progresses, presenting the player with a way to feel more powerful. This is often used in RPGs and video games.

Having some kind of post-action luck that affects the outcome of an action can also give players hope that they can do something, even if those chances are small. Otherwise, without any luck involved, they would see the situation is hopeless and lose interest in the game. At the same time, having that luck element makes predicting every possible outcome nearly impossible, so it encourages players to make a decision without spending a long time figuring out an ideal outcome.

Finally, another situation where post-action luck isn’t always a bad thing is in very short games. I love King of Tokyo even though it’s a complete dice fest with lots of post-action luck. Even if you get some really bad dice rolls, a game maybe only lasts 10-15 minutes, so it didn’t feel like a complete waste of time. On the other hand, losing a 4-hour game to a dice roll can be extremely infuriating.

The dark side of post-action luck is the human addition to random rewards, which is the reason why gambling or slot machines are so popular. Games can exploit that human quirk to their advantage and hook players in a game that would otherwise not be very interesting or fun.

A very meta post-action luck is buying “booster packs” of collectable card games (like Magic The Gathering). Purchasing the cards is the action, and the luck happens when you open it and see which random cards were in the pack. As most ex-Magic The Gathering players can attest, this can be extremely addictive.

Pre-action luck

This type of luck can add just as much randomness as post-action luck, but creates a very different feel for the game. Since the random event happens before the player action, even if you didn’t get the ideal outcome you were hoping for, you can choose to do the best action given your situation.

To illustrate the difference, consider power-ups in a first-person shooter. You open the door to one room and there’s a mysterious gift package power-up. You have no idea what it is, you pick it up and… it turns out it was health. Maybe that’s great because you were low in health. Or maybe you were maxed out and it was useless. That’s post-action luck.

Alternatively, imagine you open that door and you see 3 power-ups side by side. You see what they’re going to give you (health, ammo, or a new weapon). As soon as you take one, the others go away. Maybe neither one of them is exactly the ideal, but you can make a decision and pick the best one for your situation. That’s pre-action luck.

In board games, Stefan Feld is the master of pre-action luck. A lot of his games involve some kind of luck mechanism that limits your actions. For example, in The Castles of Burgundy or Bora Bora, you roll dice, and the numbers on those dice determine which actions you can take.

Without going that far, just about any games that involves drawing cards from a deck and having a “hand” of cards uses pre-action luck. The cards you’re dealt are the pre-action luck, and then you have to do the best you can with those cards.

An extreme type of pre-action luck is initial game layout. That happens only a single time during the game, and before players make any actions, so it has the potential to affect the full course of the game. Even players who are adamantly opposed to luck in games, are often willing to accept game setup randomness because it can be fully taken into account during the game without any surprises.

Pre-action luck isn’t as common in games as post-action luck, but it could be used just about anywhere that post-action luck is used. Consider the classic situation of a character attack some monsters and rolling a set of dice that determine whether he hits and how much damage it does. We could change that into pre-action luck by having players roll the dice (either all at once or separately), and having the dice restrict the options of what they can do. For example, low rolls on one dice could indicate that they can only do an attack close to the ground, while high rolls means they can attack flying enemies. Then the player can choose which of those actions to take, or maybe he can instead take a defensive stance or run away.

The main downside of pre-action luck is that it can extend every player action. The more it’s used, and the more possible choices it presents to the player, the longer the game might take, so it’s best to save it for times where the decisions really matter. If not, either post-action luck or no luck at all, might be better choices.

Hidden information

The most common example in board games is hidden end of game bonuses. For example, in Shipyard players get a set of goals that will score them points at the end of the game. There are two reasons for these goals: By giving each player different goals, it encourages players to focus on different aspects of the game instead of fighting over the same set of “optimal” actions. It also encourages players to pay attention to what other players are doing, and potentially try to anticipate or even block other players from getting too far ahead in their goals.

An even more interesting case is the game Troyes (one of my favorites!). Not only does each player get a set of end-of-game goals to get extra points, but all players, not just the player holding them, will be scored based on those goals. That makes paying attention to other players and trying to guess what they’re doing even more important.

At the extreme end of hidden information there are games like Discworld: Ankh-Morpork, in which each player gets a hidden winning condition. Players go about doing their actions until someone announces at the beginning of their turn that they have won the game, and they reveal their hidden victory condition card.

The higher the importance of the hidden information, the more casual and random the game becomes (and so, the shorter the game should be ideally).

篇目5,A Look at Luck in Game Design

by Darran Jamieson

The luck vs. skill aspect of games is one which is fairly central to good design—indeed, it’s something we’ve covered before. But before we worry about trying to balance luck and skill, we really need to ask: what is chance, and to what extent is it necessary in a game? Furthermore, how can we implement chance in a way that feels rewarding rather than punishing, and use it to improve rather than detract from the overall playing experience?

Is Chance Required?

It’s nearly impossible to create a game without luck. A game without luck isn’t really a game—something like “who’s the tallest” or “who has the most fingers” doesn’t really involve any sort of challenge. These are simply measurements that the players are unable to change, and so are unlikely to provide much entertainment. A game must have an element of uncertainty—something like “who can balance on one leg the longest”, while not terribly in depth, is at least not predetermined. Even when one player is better, their success is not always guaranteed.

For many games, we use cards, dice, or a random number generator to create this unpredictability. But not all games use randomisation tools, and a serious strategy game like chess still requires an element of randomness: this element comes from the players themselves. Players are unpredictable, and will often adjust strategies and tactics on the fly, based on what they consider the best probable outcome. This is why, despite being a fairly static game, chess games can vary wildly: no two players approach the game the same way.

The reason humans can provide chance to chess is because chess is incredibly complex – in fact, we can describe chess as a complex game. Unfortuntely, unlike concepts such as “flow” or “zero sum game”, the term “complex game” isn’t a recognised term. Since we’ll be talking about complexity a lot, we should probably define what we mean by it.

Understanding Complexity

So what is a complex game?

If we look at Tic-tac-toe, we can see a game with fairly simple rules. There are nine spaces, players place an X or an O, trying to make a straight line, and the game is always over in nine moves or fewer. It’s fairly easy to predict the results of a Tic-tac-toe game, even before the first move is made—assuming the two players play “correctly”, then the game will always end in a draw.

We can say then, that tic-tac-toe is hard to justify as a complex game. In fact, Tic-tac-toe has been solved, which is to say that we’ve calculated every set of possible moves, and essentially proven the best moveset. To make matters worse, humans are capable of “solving” a tic-tac-toe game without much mental agility.

Compare this to chess, which has 64 pieces and six different types of piece, each with their own moveset, special moves such as castling and en parssant, and a ruleset that means a game could (technically) last forever. Given these conditions, it’s perhaps unsurprising that chess has never been solved, even by the most powerful computers.

So, essentially, a complex game is one which has not been solved, or that cannot be solved by the players.

This addendum “cannot be solved by the players” is important. It means that games can continue to be fun, assuming the players are incapable of solving them. This is why Four-in-a-row (also known as Connect 4) remains a fairly popular game; although computers have solved it, when players sit down they are unlikely to be capable of calculating the perfect game in their heads, so they play non-optimally. Tic-tac-toe, while trivial for most players, is still a good game for young children who are unable to plot out every move in their head. Complexity is subjective.

So why is this important? Because a non-complex game (a simple game) is a boring game. If the game is not complex, then it is solvable. If it is solvable, then the outcome is predetermined; all the player is required to do is work out the best moveset, and they’ve won. And at that point, they may as well go back to playing “who has the most fingers”.

A small sidenote here, a solvable game can be better described as a puzzle. And while puzzles are popular (many newspaper print daily crossword puzzles), a puzzle is only fun up until it’s been solved – which is why crossword enthusiasts generally don’t sit and solve the same crossword over and over. Theres certainly nothing wrong in deciding to make a puzzle game, but be aware of what it is you’re aiming for, and how that will impact replayability.

Tools of Fate

So, when we look at games like chess or Tic-tac-toe, we can see they are all strategy games with no inbuilt randomisation: basic strategy games. There are, however, many games which do use dice, cards or other tools as an inbuilt mechanic, like snakes and ladders or poker. As most of these games can’t be considered complex, the inclusion of these dice or cards is necessary to prevent the game from being solvable. If, in the game of Snakes and Ladders, rather than rolling, players chose a number between one and six spaces to move every turn, then even children would quickly work out that “always choose six unless you land on a snake” is an optimal strategy.

Of course, adding randomness does not automatically make a solvable game good. In fact, you simply change the aim from “find the solution” to “find the best probable outcome”. You still essentially have a puzzle game, except that the win condition is not guaranteed. Too much randomness is just as bad as none; here again Snakes and Ladders is an obvious example. Almost no-one other than children plays the game, as it lacks any sort of interactive challenge and, therefore, people see it as ultimately pointless.

So why does a game like poker continue to work? Poker is, essentially, a series of mini puzzles. You are given a hand, and you have to “solve” how probable it is for you to win. You can then bet on your hand, based on how likely you are to win the game.

This is an oversimplified view of poker, and if this was all there was to the game, it would be fairly boring. It would be trivial to write a program to calculate the odds (although people do that anyway), and simply run it to maximise your wins.

The fun of poker comes from player interaction: from bluffing and confidence. You are not required to bet on a good hand, and you are able to bet on junk. In fact, this is arguably what the game of poker is truly about; the cards are simply there to facilitate this, and to provide a fresh round of lying every few minutes. By adding the random element, we’ve eliminated player knowledge, which means that we can use uncertainty as a game mechanic. Players are required to perform based on what they know, and it is the combination of calculating winning odds and outfoxing other players that lets poker maintain its fanbase.

Bored of Boards

So, although we’ve been talking about about traditional gaming, computer games use exactly the same principles of design. Games like Tetris or Bejewelled can be considered simple (with added randomness), and games like Starcraft or Team Fortress can be considered complex.

In almost all games, there is a certain puzzle-like quality. Even in an RTS or FPS, players are constantly making decisions based on optimal play: should I build tanks or planes? Should I choose the machine gun or grenade launcher? Should I turn left or right? Like in chess, the player attempts to make decisions based on what they think will result in the best outcome. The randomness isn’t (generally) provided by computer dice, but by the choices of the players in the game. Players are trying to outwit each other, as well as outskill them.

In fact, its possible to argue that the only randomness in a PvP game (like an FPS or RTS) should come from the players themselves. As we’ve talked about before, crits in TF2 are a subject of much contention in the playerbase—to summarise, any shot in TF2 has the possibility of being a crit shot. Crit shots require nothing more than the roll of a dice, and any damage dealt by a critical bullet will be twice or three times as much normal, which causes crits to be frequently lethal. While new players may enjoy the thrill of randomly getting a kill, “pro” players will see the crit mechanic as unnecessarily spoiling their skills.

The range of numbers we use for randomness also play a large effect on how things pan out. If a rifle deals 90-110 damage a shot, then if we have 150 health the random element is really a flavour effect: no matter what happens, we need to be shot twice to die. However, if we have 100 health, than a rifle will randomly kill us in one shot half of the time. Despite there only being a small range in randomness, the numbers used matter a great deal.

The Effects of Randomness

So why is it that “pro” players bemoan crit systems (and improperly implemented damage ranges) whereas “casual” players don’t? The answer, simply, is player expectation.

A pro player will have played their game of choice a lot. They will know it inside and out. They will know what damage they can take, what they can deal out, and what the outcome of any situation should be. And while they may sometimes judge things poorly, it is generally due to underestimating the opponent’s skill level, or making bad split-second decisions.

So when a pro player enters a battle, and they are instantly gibbed by a bullet for no reason other than luck, they might feel cheated. They knew what they wanted to happen, but because of an electronic dice roll, they were instantly killed instead.

New players will generally not feel this sting as sharply; they don’t know the game as well, they have fewer expectations of what should happen, and so they can enter a battle not really expecting to win. To them, battles are as much a learning experience as a test of skill.

This randomisation destroying expectation is something that can happen in almost any game with randomness. When you’re waiting for a line block in Tetris, and the computer instead gives you six S blocks in a row, the player might feel a little cheated. The popular game Puzzle Quest (essentially a bejewelled clone with RPG elements) received many player complaints about “cheating AI”; there are enough forum threads about it that the developers had to specifically come out and say that the AI doesn’t cheat.

So why does it feel this way? Why are so many players upset over randomly falling jewel colours? Because the randomness is subverting player expectations. When a player goes into a game, they are (generally) expecting to be challenged, but they’re also expecting that if they play well, they can win. When the game randomly throws some bad numbers at you, and you immediately lose, then you can feel cheated. You had an idea of how the game was going to play, and despite your best efforts, you were defeated—not by your own lack of skill, or superior opponent strategy, but by electronic dice. This, for most players, is incredibly infuriating.

This “luck subverting player expectation” extends into all sorts of game. In fact, the more luck involved in a game, the more likely it is to be frustrating. RPGs are a notable example, especially because of crit systems. Crits systems often seem like a fun little addition, but by the numbers they will almost always punish the players. This is because:

1.Players are, generally, expected to defeat most enemies.

2.Crits add randomness to battles.

3.Randomness in battle means unpredictable results.

4.Therefore, players will (occasionally) win battles they should have lost, but more often:

5.Players will lose battles they should have won.

This is, of course, assuming that the encounters are designed or tailored towards the player. Some RPGs simply throw the player at giant monster and be done with it; however, as professional designers, we should be looking to ensure that the game is tailored towards our players, rather than just throwing some dragons in and calling it a day.

Related Posts

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The other problem then, assuming we have designed our combats carefully, is that a crit system over-favours the player. Imagine if, after a harrowing journey through time and space, the hero of our game walks up to the ancient demon terrorising the planet and kills him in one (critical) blow. Its not quite the epic battle of legend, and is likely to leave the player feeling underwhelmed and unsatisfied. A player wants a challenge, and denying them that challenge because of randomness is unlikely to provide satisfaction.

In the case of Puzzle Quest, whether or not the AI actually was cheating isn’t actually important: what is important is that to some players, it felt like the AI was cheating. The lucky streaks gotten by the player are likely to be ignored (due to their expectation of winning anyway), but having your victory snatched away by a series of unfortunate dice rolls may seem unfair and punishing.

Fixing Things

So how do we fix things? It might seem like so far all we’ve really said is “randomness is bad”. And essentially, that’s true. We’re professional games designers; we shouldn’t be doing things randomly. Every decision the player makes should be the result of a carefully crafted experience, and putting in randomness can endanger that.

When we look more closely at it, we realise that randomness can be added for two primary reasons:

To make the outcome unpredictable, or

to generate content.

Let’s examine these:

Unpredictable Outcomes

As we talked about in our previous article, players enjoy winning.

As we talked about here, randomness somewhat replaces the need for skill.

Therefore, adding randomness to a game allows (in some sense) bad players to win against good players. In a game with no randomness, a good player will always win against a bad player.

Because of this, having this unpredictability can be an important part of a game: it allows bad players to influence the game, and (hopefully) become better. If a player is constantly matched up against superior opponents and is losing, chances are they will quickly lose interest.
However, good players will often dislike this randomness, and will often be put off by a game which “punishes” their skill.

So how can we fix this?

Well, one option is to have an Elo rating system. This essentially gives players a number based on their skill level: beat a grandmaster, and your Elo rating goes up; lose games to newbies, and your score will probably go down. It originated as a way for chess players to measure their skill, but many MOBAs (Multiplayer Online Battle Arenas, such as League of Legends and Defense of the Ancients) do this, so that when you enter a battle you are (theoretically) placed with people around the same skill level. Some first-person shooters have also attempted this, allowing players to rebalance the teams if one side is continually getting crushed.

Another option would be to have a handicap system. Not too dissimilar to an Elo system, a handicap system allows players to give themselves an artificial advantage based on their skill level. Fighting games will often do this, giving the weaker player a variable health and damage output bonus. Although a handicap system might not solve all skill imbalance issues (it’s easy to imagine online players abusing a system like this), it’s a good way to allow casual players to compete more equally with their hardcore friends.

In both these cases, you can reduce the randomness of innate game elements, leaving the players as the only randomness generators.

Generating Content

The problem with generating content randomly (or gameplay elements, like in the game of Tetris’s falling blocks) isn’t so much that we’re generating unpredictable content; it’s that, often, certain sequences of random elements are extremely punishing to the player.

If, in Tetris, the player is waiting for a line block, but we only generate S blocks for the rest of the game, then the player will have every right to be annoyed. And, while it’s improbable, it can happen.

In other games, such as a dungeon crawl RPG, we might have a 1% chance of generating a boss every time a monster spawns. If, by chance, we generate three bosses in a row, then the player might find themselves in an unwinnable battle.

By the same token, we might go through the game and never generate a boss. This could make the game incredibly easy, or (if the bosses drop equipment upgrades) incredibly hard. In either case, randomness has essentially destroyed our players’ enjoyment of the game.

In certain cases, randomness can completely remove a player from the game. In the popular collectible card game Magic: The Gathering, players build decks that require a combination of lands (power sources) and spells to defeat their opponents. The use of lands is an important balancing mechanism: a simple goblin might require one land in play, while a mighty dragon might require ten. However, if the player happens to draw no land cards, then they are unable to play anything; they are essentially forced to sit there with zero options until their opponent defeats them. While it’s possible to mitigate this to some extent, it’s a serious design flaw that a non-insignificant number of game losses are the result of bad luck, rather than being outplayed.

People feel loss more strongly than they feel gain. It’s an interesting psychological phenomenon; consider these two scenarios:

You are given $1,000. I ask if you want to gamble on a coin toss: heads you win an extra $1,000, tails you don’t. Alternatively, you can just have an extra $500 (no coin toss required).

You are given $2,000. I ask if you want to gamble on a coin toss: tails you lose $1,000, heads you don’t. Alternatively, you can just give back $500 (no coin toss required).
In general, people tend to take the guaranteed extra $500 in the first case, but gamble on the coin toss in the second… even though the outcomes for gambling are the same in each scenario! (Do the maths: whether you choose to flip the coin or not, and whether the coin comes up heads or not, the amount of money you end up with in the end is the same regardless of whether we’re talking about Option 1 or Option 2.)

This means that, if you have a mechanic in game which randomly rewards or punishes they players, the losses will, psychologically, outweigh the gains. If the gamble is optional then it opens up extra avenues of gameplay, but a forced gamble will mostly feel like punishment.

A final problem with generating content randomly is that it can be very difficult to generate content which is interesting. A great example of this is MMOs; World of Warcraft has dozens upon dozens of dungeons, each of which can take hours to complete, and weeks to successfully master. However, once the dungeons are mastered, they (arguably) offer few variations and little replayability, save for the obvious grind for equipment. In Anarchy Online, the number of designed dungeons was tiny: however, players could enter randomised dungeons. In theory, no two dungeons the players encountered would ever be the same: however, in practise, every dungeon felt the same. Because dungeons were randomised, they had no narrative structure or overall design concept. Instead of feeling unique, every dungeon felt the same.

A lot of this is down to how many rules are put in place and how the generation is implemented: Nethack and Spelunky both use randomly generated levels, and have massive fan bases. The generation of rules for interesting map design is, however, a slightly different issue from randomly generating gameplay elements, and is probably best left for another discussion; it suffices to say that a good designer should be aware of the limitations of generating maps. We can still apply much of this randomness discussion to the generation of these maps, however.

A More Serious Solution

So where does this leave us? Well, sometimes we can actually just remove randomness from a game entirely. In the case of an RPG, instead of spawning a boss 1% of a time, we can spawn them after every 100 kills. In the case of collectible card games, one of Magic’s competitors (Versus system) solved the issue of players needing land by making every card playable, face-down, as a land instead of a spell; this meant that you would never find yourself “stuck”, while maintaining the momentum that lands crucially provided.

However, a total removal of randomness can often be an overzealous case of throwing the baby out with the bath water. In the example of the RPG, making a boss spawn every 100 kills exactly is likely to make them too predictable, and when a game gets too predictable, it becomes a puzzle. A better option would be making a boss spawn somewhere between every 50 and 150 kills. This means that bosses are still within a random range (making them hard to predict), but aren’t so random you can get attacked by three at once.

This use of carefully controlled numbers is pseudorandom generation. There are many ways to do it: in Tetris, if we spawn an L block, then for the next three blocks we “re-roll the dice” once if an L block is spawned again. Normally, the L block has a one-in-seven chance of spawning, but giving it a re-roll makes it a 1/49 chance for those threee turns. It can still happen, but is much less likely.

This isn’t the best way, of course: there are many ways to generate numbers randomly, ranging from simple re-rolls to weighted random numbers; plus, sometimes, in a case like Tetris, just leaving it as a one-in-seven chance to generate any block might be the best option.

If we do use randomness, we also have the opportunity to introduce seeds. This simply means that the random numbers we use in our game aren’t actually random; the “random” sequence is entirely defined by a number called a seed. In Tetris, it appears that we can’t predict what blocks are going to fall; however, if we seed a tetris game with the number 42, and we start off with square, L block, T block, square, then every game that uses the 42 seed will begin with those blocks. Seeds aren’t used often in gaming (Minecraft and FreeCell being two notable examples), but can be a nice addition.

The ability to seed randomness comes from the fact that computers aren’t actually capable of generating random numbers: often, they simply take a base number (such as the time in milliseconds), and then perform a calculation to get a “random” number. By ensuring the base number is the same every time, the calculations will give us the same “random” numbers.

The alternative to removing randomness is to use more of it. This might seem crazy initially, but can actually be extremely effective: roll two dice, add them together, and you have a one in six chance of getting a seven. Roll 2,000 dice, add them together, divide by 1,000 (and round), and you will almost always get a seven. In this case, using so much randomness has almost entirely removed randomness.

Summing Up

At the end of the day, randomness isn’t inherently evil; it all comes down to perception. Players want to be be challenged, or to have an interesting experience, and there’s nothing challenging or interesting about throwing a bunch of random monsters at a player, with little regard to whether they live or die. By tempering the randomness, we can craft the results we want, and hopefully make a game which interacts with the player, rather than ignoring them.

A Few Final Notes About Randomness

Randomness is generally a bad way to solve conflict, because (by its very nature) it creates unpredictable results. Randomness can also be a poor way to generate content, but is often the only sensible way to approach it; imagine designing a Tetris game which had a list of every block that should drop, in order.

When randomness does occur, it should generally favour the player (which is hard to achieve in a player vs player environment). If game elements are generated randomly, they should allow the player to react to a worst case scenario in a way that still allows a reasonable chance of success.

However, we have to accept that sometimes randomness is necessary. RPGs would be a lot less exciting without dice to determine combat. Board games, in particular, are unlikely to resolve the issue anytime soon: throwing 1,000 dice and averaging out just isn’t practical for a game of Snakes and Ladders.

And of course, the final big caveat: if we remove randomness entirely, are we making a game, or a puzzle?

篇目6,Random Thoughts on Randomization in Game Design

Josh Bycer

When it comes to game design, randomization is one of my favorite words. When used properly, it enhances a game’s replay-ability dramatically. Classic titles like X-Com and Diablo 2 make excellent use of randomization to keep gamers playing, and the rogue-like genre is famous for its use of randomization. Recent indie titles:

Dungeons of Dredmor, Din’s Curse and Space Pirates And Zombies each use randomization and are examples of the pros and cons of it.

Before we talk about the pros and cons of randomization, it’s important to define the degrees of randomization that can be implemented in a game. The degrees are not ranked in terms of preference, but just the ways that a designer can have randomization.

Low: Just equipment placement and probability of finding them. Action RPGs usually have this degree of randomization. Note, you can still have important items in set locations and have common items and equipment randomized.

Medium: Enemy placement along with the low category. There are two ways of implementing this, first is with having “unique” enemies. In Diablo 2, there was a chance of running across an enemy who had a name, these enemies looked different from their cohorts and had a unique modifier such as: increase damage, fire resistance, etc, and the other way is randomizing enemy positions as well.

High: Everything in the last two categories, along with world randomization. Rogue-likes fit the bill here, but this category is not mutually exclusive to rogue-likes. Space Pirates And Zombies allows players to create a random Universe from the get-go. TBS games like Civilization also allow players a chance to play on a randomized world, or preset map conditions.

With that out of the way, let’s move on to the pros of randomization. First, is that randomization is a great way to have replay-ability in your game. You’ll never know what that treasure chest will have, or what is behind the next door, and that can be an excellent motivator to keep playing. The more elements that are randomized in the game, the longer the experience will stay fresh.

While talking to a friend about Rogue-likes he told me that to him, they were like a slot-machine. In a way he is right with that analogy, you never know if you’re going to get lucky and get all the equipment you need and blaze through the game, or if the odds are going to be stacked against you.

Randomization can also be used as a difficulty modifier, allowing the game to generate a smaller or easier world for newcomers, or a larger more challenging world for experts. This is something that Din’s Curse does well, as players can choose the levels of the enemies, how big the world is, among other factors.

With that said, there are some cons to randomization. Going back to the slot machine analogy, while the lure of a jackpot can be motivating, losing thirty times before you get there can be demoralizing. In Dungeons of Dredmor, playing the game at the hardest difficulty setting, it felt like 9 out of my 10 runs ended before I even got off the first floor due to unlucky enemy and equipment placement.

Depending on the degree of randomization, it’s very easy to generate maps that completely screw the player. Going back to Din’s Curse, there were plenty of times that the game spawns hordes of enemies at the entrance to the dungeon that overwhelmed me or having a boss appearing on the very first floor with the hardest modifiers attached to it. Din’s Curse also features modifiers to the world that makes things harder; getting stuck with the worse modifiers at the beginning can be a big hole for the player to crawl out of.

Next, is that when it comes to randomized levels, most often quality takes a hit. Creating a randomize level using functions and basic assets is easy, creating a level that not only looks aesthetically pleasing and is crafted well is another story.

The perfect example of this would be Demon’s Souls, while you could argue that randomized levels would have helped the game, no one can say that the levels weren’t carefully designed. Each level was developed with a specific challenge in mind to the point that each level had its own mood and style. From the vertigo inducing heights of stage 3-2, to the poison gauntlet of 5-2, you could tell that the designers went to great effort to design the levels.

The first poor example that I had with randomized levels was with Phantasy Star Online for the Dreamcast. In the game, every dungeon’s layout was randomized, but the game only had a few room assets per world. What this meant was that every floor had maybe 4 different room models and that’s it. The level design had a very “Frankenstein” feel, in the way that the level design felt like it was just stitched together from various elements. That is also something you want to avoid when creating randomized levels as there should be a sense of cohesion in how the world is set up. The first retail build for Dungeons of Dredmor had door assets show up where rooms were supposed to be, and it brought the quality down somewhat.

The second problem is that the more game mechanics you have in mind, the harder it will be to create a decent system. The reason is that, the more mechanics the player has access to, the more variables will have to be programmed and implemented into the engine. In Mine Craft, on each new game, the world is randomly generated for scratch and it works because the only interactions the player has is putting a block down, interacting with objects and attacking.

Let’s say that someone designs a randomized level for Deus Ex: Human Revolution, in order for that level to work, the engine must be able to create a randomized setting that must also allow for the variations in play-style, meaning it must have breakable walls, vents, areas to reach and terminals to hack. If it doesn’t have all these elements and have them presented in a way that allows progress, then players will become very frustrated if the game gives them a level with a solution that is impossible for their build.

To create a randomized system that works, it has to be built on top of a layer of linearity. What that means, is that for every random element, there must be something guaranteed. For example, in Dungeons of Dredmor, while the world is randomized each time, enemy types are limited for the most part, to specific floors. You will never see an enemy who appears on floor 5, on floor 1 and vice versa.

Same goes for Din’s Curse, on every floor no matter what; there will be a way up, along with a portal back to town. In terms of enemies, the enemies will progressively get stronger the further the player descends into the dungeon based on what level the player set as the starting level at world generation.

Going back to Mine Craft, while the world is completely randomized at creation, the same basic rules apply to each new world: better materials are found deeper underground, enemies spawn in darkness and the player has complete freedom of where to go. With these three constants, the player still experiences the world fresh each time due to the scale of the randomization.

For one of my game ideas I envisioned the game taking place in a randomized world. Where ever the player is placed, the world will be built in a sense around that position. Incredibly dangerous areas would be further away, while easier areas will be closer. The majority of the buildings will be randomized, while special buildings that act more like dungeons are linear in their design. This will allow players to experience the game differently each time, but still have a sense of progression that they can base their play through on.

A well designed randomization system can be the cherry on top for your game design, giving players added value. However, like all good mechanics, it must be properly designed and implemented.


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