Solving DCOPs with Distributed Large Neighborhood Search

Ferdinando Fioretto, Agostino Dovier, Enrico Pontelli, William Yeoh, Roie Zivan

Research output: Working paper/PreprintPreprint

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Abstract

The field of Distributed Constraint Optimization has gained momentum in recent years, thanks to its ability to address various applications related to multi-agent cooperation. Nevertheless, solving Distributed Constraint Optimization Problems (DCOPs) optimally is NP-hard.Therefore, in large-scale, complex applications, incomplete DCOP algorithms are necessary. Current incomplete DCOP algorithms suffer of one or more of the following limitations: they (a) find local minima without providing quality guarantees; (b) provide loose quality assessment; or (c) are unable to benefit from the structure of the problem, such as domain-dependent knowledge and hard constraints.Therefore, capitalizing on strategies from the centralized constraint solving community, we propose a Distributed Large Neighborhood Search(D-LNS) framework to solve DCOPs. The proposed framework (with its novel repair phase) provides guarantees on solution quality, refining upper and lower bounds during the iterative process, and can exploit domain-dependent structures. Our experimental results show that D-LNS outperforms other incomplete DCOP algorithms on both structured and unstructured problem instances.
Original languageEnglish
PublisherarXiv:1702.06915 [cs.AI]
StatePublished - 2017

Keywords

  • Computer Science - Artificial Intelligence

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