Modeling motivation for adaptive nonplayer characters in dynamic computer game worlds

Modeling motivation for adaptive nonplayer characters in dynamic computer game worlds
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动态计算机游戏世界中自适应非玩家角色的动机建模

DOI:
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发表时间:
2008
期刊:
Conference on Computability in Europe
影响因子:
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通讯作者:
K. Merrick
K. Merrick
中科院分区:
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文献类型:
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作者:
K. Merrick

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被引文献

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当前的电脑游戏被设定在日益复杂和动态的虚拟环境中。例如,大型多人在线游戏是在持久的虚拟世界中进行的,随着玩家创建和个性化他们自己的虚拟财产,这些虚拟世界会演变和变化。相比之下,用于控制虚拟游戏世界中非玩家角色行为的技术往往局限于预先编程的规则。使用固定规则集的角色缺乏随环境及时适应的能力。激励强化学习为角色设计提供了一种替代方案,可以实现既能在动态环境中进化又能适应的非玩家角色。本文提出并评估了两种用于持久电脑游戏世界中非玩家角色的激励计算模型。这些模型将激励表示为对新奇、兴趣和能力的持续追求。引入了两个指标来评估由使用不同激励模型的激励强化学习智能体控制的角色的适应性。这些指标根据所学行为的多样性和复杂性来描述非玩家角色的行为。对模拟游戏场景中角色的实证评估表明,受追求能力激励的角色在动态环境中比仅受兴趣和新奇激励的角色更具适应性。
Current computer games are being set in increasingly more complex and dynamic virtual environments. Massively multiplayer online games, for example, are played in persistent virtual worlds, which evolve and change as players create and personalize their own virtual property. In contrast, technologies for controlling the behavior of nonplayer characters that populate virtual game worlds are frequently limited to preprogrammed rules. Characters using fixed rule-sets lack the ability to adapt in time with their environment. Motivated reinforcement learning offers an alternative to character design that can achieve nonplayer characters that both evolve and adapt in dynamic environments. This article presents and evaluates two computational models of motivation for use in nonplayer characters in persistent computer game worlds. These models represent motivation as an ongoing search for novelty, interest, and competence. Two metrics are introduced to evaluate the adaptability of characters controlled by motivated reinforcement learning agents using different models of motivation. These metrics characterize the behavior of nonplayer characters in terms of the variety and complexity of learned behaviors. An empirical evaluation of characters in simulated game scenarios shows that characters motivated by the search for competence are more adaptable in dynamic environments than those motivated by interest and novelty alone.