Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/81996
Title: A hybrid search agent in Pommerman
Authors: Zhou, Hongwei
Gong, Yichen
Mugrai, Luvneesh
Khalifa, Ahmed
Nealen, Andy
Togelius, Julian
Keywords: Search engines -- Programming
Artificial intelligence
Monte Carlo method
Issue Date: 2018
Publisher: Association for Computing Machinery
Citation: Zhou, H., Gong, Y., Mugrai, L., Khalifa, A., Nealen, A., & Togelius, J. (2018). A hybrid search agent in Pommerman. FDG '18: Proceedings of the 13th International Conference on the Foundations of Digital Games, Malmö.
Abstract: In this paper, we explore the possibility of search-based agents in games with resource-intensive forward models. We implemented a player agent in the Pommerman framework and put it against the baseline agent to measure its performance. We implemented a heuristic agent and improved it by enabling depth-limited tree search in specific gameplay moments. We also compared different node selection methods during depth-limited tree search. Our result shows that depth-limited tree search is still viable when presented with inefficient forward models and exploitation-driven selection method is the most efficient in this specific domain.
URI: https://www.um.edu.mt/library/oar/handle/123456789/81996
ISBN: 9781450365710
Appears in Collections:Scholarly Works - InsDG

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