  Privacy has traditionally been a major motivation for decentralized problem
solving. However, even though several metrics have been proposed to quantify
it, none of them is easily integrated with common solvers. Constraint
programming is a fundamental paradigm used to approach various families of
problems. We introduce Utilitarian Distributed Constraint Satisfaction Problems
(UDisCSP) where the utility of each state is estimated as the difference
between the the expected rewards for agreements on assignments for shared
variables, and the expected cost of privacy loss. Therefore, a traditional
DisCSP with privacy requirements is viewed as a planning problem. The actions
available to agents are: communication and local inference. Common
decentralized solvers are evaluated here from the point of view of their
interpretation as greedy planners. Further, we investigate some simple
extensions where these solvers start taking into account the utility function.
In these extensions we assume that the planning problem is further restricting
the set of communication actions to only the communication primitives present
in the corresponding solver protocols. The solvers obtained for the new type of
problems propose the action (communication/inference) to be performed in each
situation, defining thereby the policy.
