Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2015

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 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.

Keywords

Algorithms, Artificial intelligence, Benchmarking, Constrained optimization

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 24th International Joint Conference on Artificial Intelligence IJCAI 2015: Buenos Aires, Argentina, 25-31 July

First Page

411

Last Page

417

ISBN

9781577357384

Publisher

AAAI Press

City or Country

Palo Alto, CA

Download

Share

COinS