Conference Proceeding Article
Multiagent sequential decision making has seen rapid progress with formal models such as decentralized MDPs and POMDPs. However, scalability to large multiagent systems and applicability to real world problems remain limited. To address these challenges, we study multiagent planning problems where the collective behavior of a population of agents affects the joint-reward and environment dynamics. Our work exploits recent advances in graphical models for modeling and inference with a population of individuals such as collective graphical models and the notion of finite partial exchangeability in lifted inference. We develop a collective decentralized MDP model where policies can be computed based on counts of agents in different states. As the policy search space over counts is combinatorial, we develop a sampling based framework that can compute open and closed loop policies. Comparisons with previous best approaches on synthetic instances and a real world taxi dataset modeling supply-demand matching show that our approach significantly outperforms them w.r.t.solution quality.
Artificial Intelligence and Robotics | Computer Sciences
Information Systems and Management
AAAI Conference on Artificial Intelligence AAAI 2017: San Fransisco, CA, February 4-9
City or Country
Menlo Park, CA
NGUYEN, Duc Thien; Akshat KUMAR; and LAU, Hoong Chuin.
Collective multiagent sequential decision making under uncertainty. (2017). AAAI Conference on Artificial Intelligence AAAI 2017: San Fransisco, CA, February 4-9. 3036-3043. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3529
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