Conference Proceeding Article
Multiagent sequential decision making has seen rapidprogress with formal models such as decentralized MDPsand POMDPs. However, scalability to large multiagent systemsand applicability to real world problems remain limited.To address these challenges, we study multiagent planningproblems where the collective behavior of a populationof agents affects the joint-reward and environment dynamics.Our work exploits recent advances in graphical modelsfor modeling and inference with a population of individualssuch as collective graphical models and the notion of fi-nite partial exchangeability in lifted inference. We developa collective decentralized MDP model where policies can becomputed based on counts of agents in different states. Asthe policy search space over counts is combinatorial, we developa sampling based framework that can compute openand closed loop policies. Comparisons with previous best approacheson synthetic instances and a real world taxi datasetmodeling supply-demand matching show that our app
Computer and Systems Architecture | Databases and Information Systems | Systems Architecture
Information Systems and Management
AAAI Conference on Artificial Intelligence (AAAI): San Fransisco, USA, 2017 February 4
City or Country
San Fransisco, USA
NGUYEN DUC THIEN; Akshat KUMAR; and LAU, Hoong Chuin.
Collective multiagent sequential decision making under uncertainty. (2017). AAAI Conference on Artificial Intelligence (AAAI): San Fransisco, USA, 2017 February 4. 3036-3043. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3529
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