W. Yeoh, P. Varakantham, X. Sun and S. Koenig. Incremental DCOP Search Algorithms for Solving Dynamic DCOP Problems. In Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT), 2015.

Abstract: Distributed constraint optimization (DCOP) problems are well-suited for modeling multi-agent coordination problems. However, it only models static problems, which do not change over time. Consequently, researchers have introduced the Dynamic DCOP (DDCOP) model to model dynamic problems. In this paper, we make two key contributions: (a) a procedure to reason with the incremental changes in DDCOP problems and (b) an incremental pseudo-tree construction algorithm that can be used by DCOP algorithms such as any-space ADOPT and any-space BnB-ADOPT to solve DDCOP problems. Due to the incremental reasoning employed, our experimental results show that any-space ADOPT and any-space BnB-ADOPT are up to 42 percent and 38 percent faster, respectively, with the incremental procedure and the incremental pseudo-tree reconstruction algorithm than without them.

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