The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Interleaved Backtracking in Distributed Constraint Networks
ICTAI '01 Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence
Solving Distributed Constraint Optimization Problems Using Cooperative Mediation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
The Effect of Policies for Selecting the Solution of a DisCSP on Privacy Loss
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Autonomous Agents and Multi-Agent Systems
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
The function of time in cooperative negotiations
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Protecting privacy through distributed computation in multi-agent decision making
Journal of Artificial Intelligence Research
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We merge two popular optimization criteria of Distributed Constraint Optimization Problems (DCOPs) -- reward-based utility and privacy -- into a single criterion. Privacy requirements on constraints has classically motivated an optimization criterion of minimizing the number of disclosed tuples, or maximizing the entropy about constraints. Common complete DCOP search techniques seek solutions minimizing the cost and maintaining some privacy. We start from the observation that for some problems we could provide as input a quantification of loss of privacy in terms of cost. We provide a formal way to integrate this new input parameter into the DCOP framework, discuss its implications and advantages.