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DisCSPs with Privacy Recast as Planning Problems for Self-Interested Agents

Abstract : Much of the Distributed Constraint Satisfaction Problem (DisCSP) solving research has addressed cooperating agents, and privacy was frequently mentioned as a significant motivation of the decentralization. While privacy may have a role for cooperating agents, it is easier understood in the context of self-interested utility-based agents, and this is the situation considered here. With utility-based agents, the DisCSP framework can be extended to model privacy and satisfaction under the concept of utility. We introduce Utilitarian Distributed Constraint Satisfaction Problems (UDisCSP), an extension of the DisCSP that exploits the rewards for finding a solution and the costs for losing privacy as guidance for the utility-based agents. A parallel can be drawn between Partially Observable Markov Decision Processes (POMDPs) and the problems solved by individual agents for UDisCSPs. Common DisCSP solvers are extended to take 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 ones available in the corresponding solver protocols. The solvers obtained propose the action to be performed in each situation, defining thereby the policy of the agents.
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Julien Savaux, Julien Vion, Sylvain Piechowiak, René Mandiau, Toshihiro Matsui, et al.. DisCSPs with Privacy Recast as Planning Problems for Self-Interested Agents. IEEE/WIC/ACM International Conference on Web Intelligence (WI), Oct 2016, Omaha, NE, United States. pp.359-366, ⟨10.1109/WI.2016.0057⟩. ⟨hal-03388868⟩



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