Multiply-constrained distributed constraint optimization

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Multiply-constrained distributed constraint optimization
Distributed constraint optimization (DCOP) has emerged as a useful technique for multiagent coordination. While previous DCOP work focuses on optimizing a single team objective, in many domains, agents must satisfy additional constraints on resources consumed locally (due to interactions within their local neighborhoods). Such resource constraints may be required to be private or shared for efficiency's sake. This paper provides a novel multiply-constrained DCOP algorithm for addressing these domains which is based on mutually-intervening search, i.e. using local resource constraints to intervene in the search for the optimal solution and vice versa. It is realized through three key ideas: (i) transforming n-ary constraints to maintain privacy; (ii) dynamically setting upper bounds on joint resource consumption with neighbors; and (iii) identifying if the local DCOP graph structure allows agents to compute exact resource bounds for additional efficiency. These ideas are implement...
Emma Bowring, Milind Tambe, Makoto Yokoo
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where ATAL
Authors Emma Bowring, Milind Tambe, Makoto Yokoo
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