Distributed Multi-Criteria Coordination: Privacy vs. Efficiency

Distributed constraint optimization (DCOP) has emerged as a key technique for multiagent coordination. Unfortunately, while previous work in DCOP focuses on optimizing a single team objective, domains often require satisfying additional criteria. This paper provides a novel multi-criteria DCOP algorithm, based on two key ideas: (i) transforming multi-criteria problems via virtual variables to harness single-criterion DCOP algorithms; (ii) revealing bounds on criteria to neighbors. These ideas result in interleaved multi-criteria searches, illustrated by modifying Adopt, one of the most efficient DCOP algorithms. Our Multi-Criteria Adopt algorithm (MCA) tailors its performance to whether individual constraints are to be kept private or exploited for efficiency.