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.