Presentation information

Oral presentation

General Session » [General Session] 2. Machine Learning

[1N3] [General Session] 2. Machine Learning

Tue. Jun 5, 2018 5:20 PM - 7:00 PM Room N (2F Sakurajima)

座長:松井 藤五郎(中部大学)

6:40 PM - 7:00 PM

[1N3-05] Inverse Reinforcement Learning with BDI Agents for Pedestrian Behavior Simulation

〇Nahum Alvarez1, Itsuki Noda1 (1. The National Institute of Advanced Industrial Science and Technology (AIST))

Keywords:inverse reinforcement learning, multi-agent system, pedestrian simulator

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.