Abstract
This paper discusses the problem of territory division in Hide-and-Seek games. To obtain an efficient seeking performance for multiple seekers, the seekers should agree on searching their own territories and learn to visit good hiding places first so that the expected time to find the hider is minimized. We propose a learning model using Reinforcement Learning in a hierarchical learning structure. Elemental tasks of planning the path to each hiding place are learnt in the lower layer, and then the composite task of finding the optimal sequence is learnt in the higher layer. The proposed approach is examined on a set of different maps and resulted in convergece to the optimal solution.
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Gunady, M.K., Gomaa, W., Takeuchi, I. (2012). Multi-agent Task Division Learning in Hide-and-Seek Games. In: Ramsay, A., Agre, G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2012. Lecture Notes in Computer Science(), vol 7557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33185-5_29
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DOI: https://doi.org/10.1007/978-3-642-33185-5_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33184-8
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