Modeling emotional effects on decision-making by agents in game-based simulations

Intelligent agents in games tend to exhibit behaviors that do not reflect humanlike qualities. In particular, they do not exhibit human emotional state effects on agent decision-making behavior in games. Even when emotional behavior is expressed in a few game agent architectures, such behavior is not informed by an underlying theory of emotion, nor is it quantitatively validated using human emotional behavior data. This paper presents a new emotional agent architecture that has both theoretical and experimental underpinnings, and that manifests a range of effects on behavior, especially real-time decision-making behavior. The approach is informed by the appraisal and dimensional theories of emotion, which together ensure that emotionally appraised concepts in memory correspond with the emotional state of the agent, and that such resonance produces multiple realistic effects on the agent’s decision-making behavior. The approach is validated in a series of experiments, of increasing sophistication in terms of both scenario and methods employed. The results are correlated against human data from similar cognitive science experiments. The implication of our findings is that lightweight intelligent agents can exhibit realistic humanlike behavior in arbitrarily complex real-time games in various domains.游戏中的AI智能体（AI Agent）往往表现出无法体现类人特质的行为。具体而言，它们在游戏决策行为中不会体现人类情绪状态所带来的影响。即便少数游戏智能体架构中会呈现情绪相关行为，这类行为既未依托系统的情绪理论基础，也未通过人类情绪行为数据进行量化验证。 本文提出了一种全新的情绪型智能体架构，该架构兼具理论与实验双重支撑，可对智能体行为——尤其是实时决策行为——产生多维度影响。本方法的设计灵感源自情绪评价理论与情绪维度理论，二者共同确保记忆中经情绪评价的概念与智能体的情绪状态保持一致，且这种对应关系可对智能体的决策行为产生多种符合现实的影响。 本方法通过一系列实验得到验证，这些实验在场景设置与所用方法层面均逐步提升复杂度，实验结果与同类认知科学实验中的人类行为数据具有相关性。 本研究的结论表明，轻量型AI智能体可在各领域的任意复杂实时游戏中展现出逼真的类人行为。