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Mathematics of Operations Research, 27, 819–840.īonet, B., & Geffner, H. The complexity of decentralized control of Markov decision processes. S., Givan, R., Zilberstein, S., & Immerman, N. Journal of Artificial Intelligence Research (JAIR), 9(1), 99–137.īernstein, D. Computational aspects of reordering plans. In International Joint Conference on Artificial Intelligence (IJCAI) (pp. In IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, CA, USA, July 11-17, 2009 (pp. A translation-based approach to contingent planning. Empirically, we demonstrate on benchmark domains the benefits of using these heuristics and the advantage of GPPP over existing privacy preserving planners for the multi-agent STRIPS formalism.Īlbore, A., Palacios, H., & Geffner, H. These heuristics, called privacy-preserving landmarks and privacy preserving PDBs, are agnostic to the planning algorithm and can be used by other privacy-preserving planning algorithms.
To guide GPPP, we propose two domain independent privacy preserving heuristics based on landmarks and pattern databases, which are classical heuristics for single agent search. In this work we present the Greedy Privacy-Preserving Planner (GPPP), a privacy preserving planning algorithm in which the agents collaboratively generate an abstract and approximate global coordination plan and then individually extend the global plan to executable plans. Maintaining privacy is challenging when planning for multiple agents, especially when tight collaboration is needed and a global high-level view of the plan is required. We address the problem of automatically generating such a coordination plan while preserving the agents’ privacy. To collaborate effectively, some sort of plan is needed to coordinate the different entities. In many cases several entities, such as commercial companies, need to work together towards the achievement of joint goals, while hiding certain private information.