@article {1506495,
	title = {Balancing Local Resources and Global Goals in Multiply-Constrained DCOP },
	journal = {Journal of Multiagent and Grid Systems (MAGS)},
	volume = {6},
	number = {4},
	year = {2010},
	pages = {353-393},
	abstract = {Distributed constraint optimization (DCOP) is a useful framework for
cooperative multiagent coordination. DCOP focuses on optimizing a single team objective. However, in many domains, agents must satisfy constraints on resources consumed locally while optimizing the team goal.
Yet, these resource constraints may need to be kept private. Designing
DCOP algorithms for these domains requires managing complex trade-offs
in completeness, scalability, privacy and efficiency.
This article defines the multiply-constrained DCOP (MC-DCOP) framework and provides complete (globally optimal) and incomplete (locally optimal) algorithms for solving MC-DCOP problems. Complete algorithms find the best allocation of scarce resources while optimizing the team objective, while incomplete algorithms are more scalable. The algorithms use
four main techniques: (i) transforming constraints to maintain privacy;
(ii) dynamically setting upper bounds on resource consumption; (iii) identifying the extent to which the local graph structure allows agents to compute exact bounds; and (iv) using a virtual assignment to flag problems
rendered unsatisfiable by resource constraints. Proofs of correctness are
presented for all algorithms. Experimental results illustrate the strengths
and weaknesses of both the complete and incomplete algorithms.},
	author = {Bowring, Emma and Tambe, Milind and Makoto Yokoo}
}
