Abstract
Multi-Agent Plan Recognition (MAPR) is the problem of inferring the goals and plans of multiple agents given a set of observations. While previous MAPR approaches have largely focused on recognizing team structures and behaviors, given perfect and complete observations, in this paper, we address potentially unreliable observations and temporal actions. We propose a multi-step compilation technique that enables the use of AI planning for the computation of the probability distributions of plans and goals, given observations. We present results of an experimental evaluation on a novel set of benchmarks, using several temporal and diverse planners.
M. Shvo—The work was performed during an internship at IBM.
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Acknowledgments
The authors gratefully acknowledge funding from IBM and the Natural Sciences and Engineering Research Council of Canada (NSERC).
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Shvo, M., Sohrabi, S., McIlraith, S.A. (2018). An AI Planning-Based Approach to the Multi-Agent Plan Recognition Problem. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_23
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DOI: https://doi.org/10.1007/978-3-319-89656-4_23
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