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Probabilistic Inference Based Message-Passing for Resource Constrained DCOPs

Supriyo Ghosh, Akshat Kumar, Pradeep Varakantham

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 411-416.

Abstract

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 coordination 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.

Supriyo Ghosh, Akshat Kumar, and Pradeep Varakantham. 2015. Probabilistic Inference Based Message-Passing for Resource Constrained DCOPs. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 411–416. https://ijcai.org/proceedings/2015/64
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