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开发者分享让AI富有社交吸引力的设计经验

发布时间:2012-03-09 14:25:17 Tags:,,,,

作者:Leigh Alexander

设计带有可靠AI、基于对话并由叙事驱动的游戏是个巨大的挑战,因为这需要相当复杂的系统。当对话成为主要游戏玩法机制时,玩家会很容易注意到系统的局限性,或者游戏世界的社会结构响应并不自然的情境。

但是,许多开发者依然在这片领域上探索,创造各种变量和矢量,让游戏角色感觉起来像是带有自然行为的人类。在本周的游戏开发者大会上,许多开发者在演讲中陈述了他们细节化AI系统的方法。

prom week (from pastemagazine.com)

prom week (from pastemagazine.com)

《Prom Week》在这一领域上的进步使其获得了独立游戏节提名,这款游戏由加州大学圣克鲁兹分校Expressive Intelligence Studio开发,旨在呈现出更深层次且更为复杂的社交模拟类性,而不是普通的对话驱动游戏。玩家通过与其他角色的互动来实现故事目标,他们需要在社交过程中制定有效的策略。

在《Prom Week》中,角色有着各种不同的特征,比如令人同情或傲慢自大,角色有自己的言辞方式,而且同游戏世界中的其他角色也有一定的关系。在游戏健全和复杂的AI系统中,影响与角色互动方式的主观关系值和可能改变目标的重视值也有一定的作用。

该游戏开发者是Expressive Intelligence Studio的成员Michael Treanor,他在游戏开发者大会上向众人呈现了这个系统,他说道:“我们并非尝试模拟现实,我们制作这款游戏的目标只是为了呈现特别形式的媒体体验。”

角色的响应取决于“接受或拒绝”规则,系统会根据玩家的选择做出最正确的响应。最值得关注的是,在《Prom Week》世界中,互动能够产生持续性的状态改变。其AI系统通过间接作用——社交交流的传递结果呈现了自身的独特性。

Emily Short是有关语言游戏的资深人士。她和《Cotillion》制作人Richard Evans创建了Little Text People,该工作室近期被Linden Labs收购。

《Cotillion》被描述为“礼仪互动喜剧”,游戏中呈现的是简·奥斯汀时代极为普遍的社交行为。Short想要让玩家在游戏世界中分别体验到恰当和不恰当的行为,她说道:“游戏中呈现了类似《模拟人生》的自由度,但是我们每章节的设计时限为30分钟左右。”

她解释称,这个目标令人感觉并不像是制作游戏,而更像是呈现“许多互动文字”。游戏具有即时互动功能。Short解释道:“无论你是否做出动作,AI角色都会继续自己的行为。你可以等待片刻,看看会发生什么情况,然后再参与到角色的行为中。”

《Cotillion》完成了更深层次的模拟开发挑战,它是款多人游戏,而且完全分辨不清哪些功能由玩家运转,哪些功能由NPC运转。所有角色(游戏邦注:包括由玩家控制的角色和非玩家控制的角色)都有相同的情境支持。游戏结构以相信和质疑系统为基础,如果有个角色表达了自己的观点,其他角色可以选择以标准的方式相信他并与之探讨相同的问题,或者提出质疑以及引入新的话题。

Short说道:“但是,这样的系统依然未能实现我们心目中的丰富性和多样性要求。”角色互动关系还应当带有情感成分:比如质疑他人可能会让他们感到愤怒,或者提及财富可能会让他们嫉妒。

有效内容库可以通过专门化系统进行细化:可以根据情景来设置被激怒等简单的情感层次。结果就是:根据不同情境,将相信同角色可能产生的反应,以及专门层次结合起来,这可以创造出庞大复杂的社交世界。

在此基础上,Short描述了《Cotillion》中的角色如何通过可编码的特征列表获得各种性格。比如,健谈的角色在系统中会频繁做出响应,说话的概率更大,而且该类角色还会迅速回应自己喜欢的特定话题。爱提供建议的角色则更喜欢带有“正确”标签的观点。

Short强调称:“这为我们创造了一个介于对话信息内容和情感内容的接口。我们可以生成标准的简·奥斯汀时代短对话,但是这在叙事上并不具有很强的吸引力,可能也不会受到玩家的认可。”所以,场景的生成同角色的目标有关。

Little Text People的Richard Evans表示,该做法的灵感来源于Harvey Sacks(20世纪60年代末美国社会学家)有关对话分析的理论,尤其是成员分类和话轮转换。角色可以同时扮演多种身份,比如达西先生可能是好友、兄弟以及舞会客人,而角色在每个场景中会有不同的性格特征,他可能是个很忠实的亲戚,但是在舞会上表现很糟糕,因为他不喜欢跳舞。

假设某个角色在晚餐时做出奉承的举动。系统会根据达西先生的各种角色来评估他所做出的反应。《模拟人生》会评估友好度等级和浪漫等级。与《Prom Week》相似,《Cotillion》会评估3项元素——另一项就是说不清道不明的“酷”元素,它无需指明两个角色之间的关系,就可体现角色针对他人观点和行为的应变能力。

话轮转换是个非常复杂的系统,决定了接下来说话的人。群体中的某些参与者将首先说话,如果有人不按照社交顺序打断某些人发表意见,就会出现尴尬的结果。

负责制作《Storybricks》的Stephane Bura也在努力在处理同样的设计挑战,尤其是复杂系统缩放方面的挑战。依他的观点来看,创建“意图”可以部分解决这些难以缩放的庞大行为网络的问题,“意图”能够减少创建行为所需的规则数量,因为意图可与多种属性相联系。(本文为游戏邦/gamerboom.com编译,拒绝任何不保留版权的转载,如需转载请联系:游戏邦

GDC 2012: New ideas in socially-engaging AI design

Leigh Alexander

Designing conversation-based, narrative-driven games with believable AI is a steep challenge of complex systems. When conversation is a primary gameplay mechanic, players are extra conscious of limitations on the system or of situations where the world’s social fabric doesn’t seem to respond naturally.

But many developers continue to push forward in this arena, evolving the numbers of variables and vectors that help game characters feel like individuals with naturalistic behaviors. Several of these spoke about their detailed AI systems in a series of presentations on socially-engaging AI at the Game Developers’ conference this week.

Prom Week’s strides in this arena have earned it an Independent Games Festival nomination for technical excellence; the game by UC Santa Cruz’s Expressive Intelligence Studio specifically aims for a deeper and more complex social simulation than other dialogue-driven games. Players aim for story goals by interacting with other characters, and are tasked with strategizing within the social space.

In Prom Week, characters are defined by sets of traits like compassion or arrogance, by their word choices and speech patterns, and by their pre-defined relationships with characters in the world.

Subjective relationship values – different ways characters can be close, and weight values that modify intentions also play a role in the game’s robust, complex AI system.

“We’re not trying to model reality, necessarily. We wrote these targeting a very specific kind of media experience,” explains creator Michael Treanor of UC Santa Cruz’s Expressive Intelligence Studio, presenting the system in a talk on socially engaging AI at the Game Developers Conference. That particular aim was to create a world influenced by teen media set in high schools.

Character responses are determined through an “accept or reject” rule, where the system calculates the most plausible response based on player choices. Most notably, interactions create permanent state changes in the Prom Week world. The AI system differentiates itself well through indirect effects – the cascading consequences of social exchanges.

Emily Short is a veteran of the language-based game space. She and Cotillion co-creator Richard Evans of Little Text People – the studio recently picked up by Linden Labs – spoke about Cotillion.

Described as an “interactive comedy of manners”, Cotillion plays with the prescribed social practices common in Jane Austin’s era. Short wanted to enable players to experiment with both appropriate and inappropriate behavior within the world: “We’ve got a sort of Sims-like freedom, but our design is about episodes that are about 30 minutes long.”

The aim was something that feels less like a game, and more like “a piece of interactive text,” she explains. Its interaction features are real-time: “The AI characters are continuing to act whether you are doing anything or not,” Short explains. “You can sit there and wait for a while to see what happens before you jump on in.”

In a further simulation development challenge, Cotillion is multiplayer – and is completely agnostic about which features are run by players and which are run by NPCs. All characters, whether controlled by players or non-players, have the same affordances. The structure relies on a system of beliefs and questions; if one character expresses a belief, the other character can reply in a standard way, offer a belief on the same topic, ask a question or introduce a new topic.

“But this still doesn’t get us to the level of richness and variability that we really want,” says Short. Thus beliefs are also tagged with emotional significance: for example, expressing a negative belief about someone could lead to them being insulted, or mentioning wealth could lead a character perhaps to envy you, envision you a braggart, or decide you’re a good marriage prospect.

The library of effective content can be detailed through a system of specificity: There are layers to the simple emotional state of being insulted, depending on context. The result: Combining beliefs with possible reactions and layers of specificity depending on context creates a massively complex social universe.

On top of this, Short described how characters in Cotillion attain finely-grained characterizations through lists of traits that can be encoded – for example, a character who is drawn as talkative can be designed to frequently prefer response options within the system that lead to her speaking more, and she can be engineered to favor particular topics when they arise, too. A character that likes to give advice is coded to prefer beliefs that are tagged as “correcting.”

“This gives us an interface between the informational content in a conversation and the emotional content of a conversation,” Short notes. “We can generate standard Jane Austen-esque small talk… but that’s not very narratively compelling or what you want at all times.” So scenes with higher stakes can be generating by creating expectations that characters have for their goals and stakes.

Little Text People’s Richard Evans says the work was inspired by Harvey Sacks’ work on conversation analysis, specifically membership categorization devices and turn-taking. A character can be playing many roles at once: For example, Mr. Darcy is friend, brother and ball guest, and has different traits in each setting – he could be a loyal relative, but a poor participant at the ball because he dislikes dancing.

Say a character makes a sycophantic remark at dinner. The system can evaluate how a character like Mr. Darcy would respond based on the various roles that character plays and how well he plays them. The Sims evaluates friendship level and romantic level; like Prom Week, Cotillion works on three – the nebulous “cool” factor, which suggests basically how well one character’s views and behavior agree with another, without actually indicating anything about the two characters’ relationship.

Turn-taking is a very complex system that, through a set of situational preferences, determines who will speak next; someone who’s addressed directly in a group is liable to speak first, and if someone interrupts – speaks out of social order – there may be situational consequences.

Storybricks’ Stephane Bura has been working to address some of the same design challenges as his fellow panelists, with particular attention to the challenge in scaling complex systems. That these massive behavioral webs are hard to scale can be partially addressed, in his view, by creating intentions – “intention” can reduce the number of rules required to create behavior, as an intention can be associated with multiple attributes and share the same scale.

The panelists had much to offer in terms of progress on interaction systems in games, and the room was full of optimism. Bura noted the teasing Skyrim gets from fans for its AI system, and yet:

“How awesome is it that you have characters that understand not only theft, but also the context in which theft can occur?” he reflects. (Source: Gamasutra)


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