TY - GEN
T1 - Multiply-constrained DCOP for distributed planning and scheduling
AU - Bowring, Emma
AU - Tambe, Milind
AU - Yokoo, Makoto
PY - 2006
Y1 - 2006
N2 - 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's sake. This paper provides a novel multiply-constrained 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.
AB - 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's sake. This paper provides a novel multiply-constrained 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=33747177125&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33747177125
SN - 1577352653
SN - 9781577352655
T3 - AAAI Spring Symposium - Technical Report
SP - 25
EP - 32
BT - Distributed Plan and Schedule Management - Papers from the AAAI Spring Symposium, Technical Report
T2 - 2006 AAAI Spring Symposium
Y2 - 27 March 2006 through 29 March 2006
ER -