Theory and algorithms for plan merging
Artificial Intelligence
The computational complexity of propositional STRIPS planning
Artificial Intelligence
On social laws for artificial agent societies: off-line design
Artificial Intelligence - Special volume on computational research on interaction and agency, part 2
A comparative analysis of partial order planning and task reduction planning
ACM SIGART Bulletin
Coalitions among computationally bounded agents
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Intelligent planning: a decomposition and abstraction based approach
Intelligent planning: a decomposition and abstraction based approach
Privacy preserving auctions and mechanism design
Proceedings of the 1st ACM conference on Electronic commerce
Coordinating Plans of Autonomous Agents
Coordinating Plans of Autonomous Agents
Coordinating Mutually Exclusive Resources using GPGP
Autonomous Agents and Multi-Agent Systems
Trends in Cooperative Distributed Problem Solving
IEEE Transactions on Knowledge and Data Engineering
A Market Protocol for Decentralized Task Allocation
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Modularity and communication in multiagent planning
Modularity and communication in multiagent planning
Distributed Constraint Satisfaction and Optimization with Privacy Enforcement
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
The value of privacy: optimal strategies for privacy minded agents
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Autonomous Agents and Multi-Agent Systems
Graph-based multiagent replanning algorithm
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Privacy Loss in Classical Multiagent Planning
IAT '07 Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Effective approaches for partial satisfaction (over-subscription) planning
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Mechanisms for automated negotiation in state oriented domains
Journal of Artificial Intelligence Research
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
Hierarchical planning in a distributed environment
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
Using partial global plans to coordinate distributed problem solvers
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Counting solutions of CSPs: a structural approach
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Constraint-based reasoning and privacy/efficiency tradeoffs in multi-agent problem solving
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Coordination through plan repair
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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Privacy is often cited as the main reason to adopt a multiagent approach for a certain problem. This also holds true for multiagent planning. Still, a metric to evaluate the privacy performance of planners is virtually non-existent. This makes it hard to compare different algorithms on their performance with regards to privacy. Moreover, it prevents multiagent planning methods from being designed specifically for this aspect. This paper introduces such a measure for privacy. It is based on Shannon's theory of information and revolves around counting the number of alternative plans that are consistent with information that is gained during, for example, a negotiation step, or the complete planning episode. To accurately obtain this measure, one should have intimate knowledge of the agent's domain. It is unlikely (although not impossible) that an opponent who learns some information on a target agent has this knowledge. Therefore, it is not meant to be used by an opponent to understand how much he has learned. Instead, the measure is aimed at agents who want to know how much privacy they have given up, or are about to give up, in the planning process. They can then use this to decide whether or not to engage in a proposed negotiation, or to limit the options they are willing to negotiate upon.