Multiagent-based Participatory Urban Simulation through Inverse Reinforcement Learning

2017/12/21 by Soma Suzuki, Suzuki, Soma
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Evacuation and Crowd Dynamics #FOS: Computer and information sciences #Multiagent Systems (cs.MA) #Reinforcement Learning in Robotics #Transportation and Mobility Innovations

paper · pdf · doi:10.48550/arxiv.1712.07887

openalex publication_date 2017/12/21 · openalex created_date 2018/01/05 · openalex updated_date 2026/07/28

Abstract

The multiagent-based participatory simulation features prominently in urban planning as the acquired model is considered as the hybrid system of the domain and the local knowledge. However, the key problem of generating realistic agents for particular social phenomena invariably remains. The existing models have attempted to dictate the factors involving human behavior, which appeared to be intractable. In this paper, Inverse Reinforcement Learning (IRL) is introduced to address this problem. IRL is developed for computational modeling of human behavior and has achieved great successes in robotics, psychology and machine learning. The possibilities presented by this new style of modeling are drawn out as conclusions, and the relative challenges with this modeling are highlighted.

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