TY - GEN
T1 - DisCSPs with Privacy Recast as Planning Problems for Self-Interested Agents
AU - Savaux, Julien
AU - Vion, Julien
AU - Piechowiak, Sylvain
AU - Mandiau, Rene
AU - Matsui, Toshihiro
AU - Hirayama, Katsutoshi
AU - Yokoo, Makoto
AU - Elmane, Shakre
AU - Silaghi, Marius
N1 - Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/1/12
Y1 - 2017/1/12
N2 - 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.
AB - 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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U2 - 10.1109/WI.2016.0057
DO - 10.1109/WI.2016.0057
M3 - Conference contribution
AN - SCOPUS:85013073484
T3 - Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016
SP - 359
EP - 366
BT - Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016
Y2 - 13 October 2016 through 16 October 2016
ER -