@conference {1509411,
	title = {Multiply-Constrained DCOP for Distributed Planning and Scheduling },
	booktitle = {AAAI Spring Symposium on Distributed Planning and Scheduling	},
	year = {2006},
	abstract = {Distributed constraint optimization (DCOP) has emerged as
a useful technique for multiagent planning and scheduling.
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 local resource constraints may be required to be private or shared
for efficiency{\textquoteright}s sake. This paper provides a novel multiplyconstrained DCOP algorithm for addressing these domains.
This algorithm is based on mutually-intervening search, i.e.
using local resource constraints to intervene in the search for
the optimal solution and vice versa, realized via three key
ideas: (i) transforming n-ary constraints via virtual variables
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 implemented by modifying Adopt, one of the most
efficient DCOP algorithms. Both detailed experimental results as well as proofs of correctness are presented.},
	author = {Bowring, Emma and Tambe, Milind and Makoto Yokoo}
}
