Automatic Generation of Agent Behavior Models from Raw Observational Data

Agent-based modeling is used to simulate human behaviors in different fields. The process of building believable models of human behavior requires that domain experts and Artificial Intelligence experts work closely together to build custom models for each domain, which requires significant effort. The aim of this study is to automate at least some parts of this process. We present an algorithm called magic, which produces an agent behavioral model from raw observational data. It calculates transition probabilities between actions and identifies decision points at which the agent requires additional information in order to choose the appropriate action. Our experiments using syntheticallygenerated data and real-world data from a hospital setting show that the magic algorithm can automatically produce an agent decision process. The agent’s underlying behavior can then be modified by domain experts, thus reducing the complexity of producing believable agent behavior from field data.