Modeling Human-Like Decision Making for Virtual Agents in Time-Critical Situations

Abstract

Generating human-like behaviors for virtual agents has become increasingly important in many applications, such as crowd simulation, virtual training, digital entertainment, and safety planning. One of challenging issues in behavior modeling is how virtual agents make decisions given some time-critical and uncertain situations. In this paper, we present HumDPM, a decision process model for virtual agents, which incorporates two important factors of human decision making in time-critical situations: experience and emotion. In HumDPM, rather than relying on deliberate rational analysis, an agent makes its decisions by matching past experience cases to the current situation. We propose the detailed representation of experience case and investigate the mechanisms of situation assessment, experience matching and experience execution. To incorporate emotion into HumDPM, we introduce an emotion appraisal process in situation assessment for emotion elicitation. In HumDPM, the decision making process of an agent may be affected by its emotional states when: 1) deciding whether it is necessary to do a re-match of experience cases, 2) determining the situational context, and 3) selecting experience cases. We illustrate the effectiveness of HumDPM in crowd simulation. A case study for emergency evacuation in a subway station scenario is conducted, which shows how a varied crowd composition leads to different evacuation behaviors, due to the retrieval of different experiences and the variation of agents' emotional states.