Outline

  • Abstract
  • Keywords
  • 1. Introduction
  • 2. Previous Work
  • 2.1. Cognitive Modeling
  • 2.2. Emotional Modeling and Decision
  • 2.3. Previous Computational Models
  • 3. New Emotional Agent Architecture and Operational Concept
  • 4. Experiments and Results
  • 5. Future Work
  • References

رئوس مطالب

  • چکیده
  • کلید واژه ها
  • 1. مقدمه
  • 2. آثار قبلی
  • 1.2. مدل سازی ادراکی
  • 2.2. مدل سازی هیجانی و تصمیم
  • 3.2. مدل های محاسباتی قبلی
  • 3. مفهوم عملیاتی و معماری عامل هیجانی جدید
  • 4. آزمایشات و نتایج
  • 5. کار آینده

Abstract

Game AI agents today do not reflect the affective aspects of human behavior. In particular, game agents do not reflect the effects of human emotional state on an agent’s decision-making behavior. In rare instances when emotional aspects are addressed in game agent architectures, such behavior tends to be ad hoc and not informed by an underlying theory of emotion, nor validated using actual data. This paper presents a new emotional game agent architecture that is based on an underlying theory of emotion and validated by limited experiments. This architecture manifests a range of emotional effects on game agent behaviors. The overall approach is informed by both appraisal and dimensional theories of emotion. The combination of these theories as underpinnings ensures that emotionally appraised concepts in memory are reflected in the emotional state of the agent, and that such correspondence produces realistic emotional effects on the agent’s decision-making behavior. The approach is validated through a series of increasingly more sophisticated experiments, in terms of scenario complexity and methods employed. The results are correlated with human data from previous cognitive science experiments. The results show that “lightweight” intelligent agents based on the new game agent architecture can exhibit realistic emotional behavior in real-time decision-making situations encountered in games across various domains.

Keywords: - - - - -

Future Work

This paper has presented a novel agent architecture that incorporates emotional effects in agent decision making. The model and architecture are framed in game AI with the intent of making game AI agents more humanlike. Several experiments were run to calibrate and then validate the agent model. To present a richer decision-making process integrated more tightly with emotional state, the implemented experiments can be enhanced. Particularly, the Nuclear Power Plant experiment can be upgraded to include multiple and sequential states and actions in memory, maintained in an interaction matrix. The decision-maker would then have several hypotheses to choose from to determine the cause of low water pressure. Also, the decision-maker would use faultmodel analysis based on emotionally dependent probabilities to make its decisions, instead of a strict utility function.

The revised experiments can provide an opportunity to generate feedback changes to emotional state. Thus, in the AX experiment, these changes would be based on the appearance of an emotionally charged letter and/or bigram. The new Tower of London experiment would include alteration of emotional state based on repeated moves and positions. Emotional state may also change based on the agent getting closer to or further from the solution based on heuristic assessment. In the nuclear power plant experiment, the emotional state would change depending on the criticality of readings as well as the success or failure of actions taken. Dynamic emotional state is the first step towards a more dynamic system, culminating in experiential and emotional learning, and in case-based reasoning. These advances are expected to improve the behavior of agents in games, making them appear more credible and thereby making gameplay more realistic.

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