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
Negotiation in Semi-cooperative Agreement Problems
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Quantifying privacy in multiagent planning
Multiagent and Grid Systems - Planning in multiagent systems
An agent reinforcement learning model based on neural networks
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Improving DPOP with function filtering
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Cluster Tree Elimination for Distributed Constraint Optimization with Quality Guarantees
Fundamenta Informaticae - RCRA 2008 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
Speculative constraint processing for hierarchical agents
AI Communications - European Workshop on Multi-Agent Systems (EUMAS) 2009
Privacy-preserving activity scheduling on mobile devices
Proceedings of the first ACM conference on Data and application security and privacy
Meetings through the cloud: Privacy-preserving scheduling on mobile devices
Journal of Systems and Software
Protecting privacy through distributed computation in multi-agent decision making
Journal of Artificial Intelligence Research
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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 framework 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.