Proceedings of the Annual Conference of JSAI
32nd Annual Conference, 2018
Session ID : 1N3-05
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Inverse Reinforcement Learning with BDI Agents for Pedestrian Behavior Simulation
*Nahum ALVAREZItsuki NODA
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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.

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© 2018 The Japanese Society for Artificial Intelligence
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