Inverse Reinforcement Learning with BDI Agents for Pedestrian Behavior Simulation

Abstract

Crowd behavior has been subject of study in fields like disaster evacuation, smart town planning and business strategic placing. It is possible to create a model for those scenarios using machine learning techniques and a relatively small training data set to identify behavioral. We implemented a BDI-based agent model that uses such techniques into a large-scale crowd simulator, and apply inverse reinforcement learning to adjust agents' behaviors by examples. The goal of the system is to provide to the agents a realistic behavior model and a method to orient themselves without knowing the scenario's layout, based in learnt patterns around environment features.