Synthesizing constraint expressions
Communications of the ACM
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Developing an Automated Distributed Meeting Scheduler
IEEE Expert: Intelligent Systems and Their Applications
Representing Possibilities in Relation to Constraints and Agents
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Asynchronous Search with Aggregations
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
SSDPOP: improving the privacy of DCOP with secret sharing
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Using relaxations to improve search in distributed constraint optimisation
Artificial Intelligence Review
Agent-Based Speculative Constraint Processing
IEICE - Transactions on Information and Systems
Analysis of privacy loss in distributed constraint optimization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
BnB-ADOPT+ with Several Soft Arc Consistency Levels
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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Because of privacy concerns, agents may not want to reveal information that could be of use in problem solving. As a result, there are potentially important tradeoffs between maintaining privacy and enhancing search efficiency in these situations. In this work we show how quantitative assessments of privacy loss can be made within the framework of distributed constraint satisfaction. We also show how agents can make inferences about other agents' problems or subproblems from communications that carry no explicit private information. This can be done using constraint-based reasoning in a fiamework consisting of an ordinary CSP, which is only partly known, and a system of "shadow CSPs" that represent various forms of possibilistic knowledge. This kind of reasoning in combination with arc consistency processing can speed up search under conditions of limited communication, at the same time potentially undermining privacy. These effects are demonstrated in a simplified meeting scheduling problem where agents propose meetings consistent with their existing schedules while responding to other proposals by accepting or rejecting them. In this situation, we demonstrate some of the conditions under which privacy/efficiency tradeoffs emerge, as well as complications that arise when agents can reason effectively under conditions of partial ignorance.