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Privacy has traditionally been a major motivation for decentralized problemsolving. However, even though several metrics have been proposed to quantifyit, none of them is easily integrated with common solvers. Constraintprogramming is a fundamental paradigm used to approach various families ofproblems. We introduce Utilitarian Distributed Constraint Satisfaction Problems(UDisCSP) where the utility of each state is estimated as the differencebetween the the expected rewards for agreements on assignments for sharedvariables, and the expected cost of privacy loss. Therefore, a traditionalDisCSP with privacy requirements is viewed as a planning problem. The actionsavailable to agents are: communication and local inference. Commondecentralized solvers are evaluated here from the point of view of theirinterpretation as greedy planners. Further, we investigate some simpleextensions where these solvers start taking into account the utility function.In these extensions we assume that the planning problem is further restrictingthe set of communication actions to only the communication primitives presentin the corresponding solver protocols. The solvers obtained for the new type ofproblems propose the action (communication/inference) to be performed in eachsituation, defining thereby the policy.

DisCSPs with Privacy Recast as Planning Problems for Utility-based Agents