Abstract
Multi-agent planning (MAP) has recently gained traction in both planning and multi-agent system communities, especially with the focus on privacy-preserving multi-agent planning, where multiple agents plan for a common goal but with private information they do not want to disclose. Heuristic search is the dominant technique used in MAP and therefore it is not surprising that a significant attention has been paid to distributed heuristic computation, either with or without the concern for privacy. Nevertheless, most of the distributed heuristic computation approaches published so far are ad-hoc algorithms tailored for the particular heuristic. In this work we present a general, privacy-preserving, and admissible approach to distributed heuristic computation. Our approach is based on an adaptation of the technique of cost partitioning which has been successfully applied in optimal classical planning. We present the general approach, a particular implementation, and an experimental evaluation showing that the presented approach is competitive with the state of the art while having the additional benefits of generality and privacy preservation.
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Acknowledgments
This research was supported by the Czech Science Foundation (grant no. 18-24965Y). The authors acknowledge the support of the OP VVV MEYS funded project CZ.02.1.01/0.0/0.0/16_019/0000765 “Research Center for Informatics”.
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Štolba, M., Urbanovská, M., Fišer, D., Komenda, A. (2019). A General Approach to Distributed and Privacy-Preserving Heuristic Computation. In: van den Herik, J., Rocha, A., Steels, L. (eds) Agents and Artificial Intelligence. ICAART 2019. Lecture Notes in Computer Science(), vol 11978. Springer, Cham. https://doi.org/10.1007/978-3-030-37494-5_4
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