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Privacy has traditionally been a major motivation for distributed problemsolving. Distributed Constraint Satisfaction Problem (DisCSP) as well asDistributed Constraint Optimization Problem (DCOP) are fundamental models usedto solve various families of distributed problems. Even though severalapproaches have been proposed to quantify and preserve privacy in suchproblems, none of them is exempt from limitations. Here we approach the problemby assuming that computation is performed among utilitarian agents. Weintroduce a utilitarian approach where the utility of each state is estimatedas the difference between the reward for reaching an agreement on assignmentsof shared variables and the cost of privacy loss. We investigate extensions tosolvers where agents integrate the utility function to guide their search anddecide which action to perform, defining thereby their policy. We show thatthese extended solvers succeed in significantly reducing privacy loss withoutsignificant degradation of the solution quality.

Distributed Constraint Problems for Utilitarian Agents with Privacy Concerns, Recast as POMDPs