Probabilistic Inference Based Message-Passing for Resource Constrained DCOPs

• Main Authors: GHOSH, Supriyo, Akshat KUMAR, Pradeep VARAKANTHAM
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Algorithms; Artificial intelligence; Benchmarking; Constrained optimization; Artificial Intelligence and Robotics; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3155; https://ink.library.smu.edu.sg/context/sis_research/article/4155/viewcontent/P_ID_52423_EMDCOP.pdf
• Institution: Singapore Management University

Distributed constraint optimization (DCOP) is an important framework for coordinated multiagent decision making. We address a practically useful variant of DCOP, called resource-constrained DCOP (RC-DCOP), which takes into account agents’ consumption of shared limited resources. We present a promising new class of algorithm for RC-DCOPs by translating the underlying co- ordination problem to probabilistic inference. Using inference techniques such as expectation- maximization and convex optimization machinery, we develop a novel convergent message-passing algorithm for RC-DCOPs. Experiments on standard benchmarks show that our approach provides better quality than previous best DCOP algorithms and has much lower failure rate. Comparisons against an efficient centralized solver show that our approach provides near-optimal solutions, and is significantly faster on larger instances.