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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 359-366 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781509044702 |
| DOIs | |
| Publication status | Published - Jan 12 2017 |
| Event | 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016 - Omaha, United States Duration: Oct 13 2016 → Oct 16 2016 |
Publication series
| Name | Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016 |
|---|
Other
| Other | 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016 |
|---|---|
| Country | United States |
| City | Omaha |
| Period | 10/13/16 → 10/16/16 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
Cite this
DisCSPs with Privacy Recast as Planning Problems for Self-Interested Agents. / Savaux, Julien; Vion, Julien; Piechowiak, Sylvain; Mandiau, Rene; Matsui, Toshihiro; Hirayama, Katsutoshi; Yokoo, Makoto; Elmane, Shakre; Silaghi, Marius.
Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 359-366 7817074 (Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
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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UR - http://www.scopus.com/inward/citedby.url?scp=85013073484&partnerID=8YFLogxK
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.
ER -