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Creators/Authors contains: "Gong, Yichen"

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  1. ; ; ; ; ; ; ; (, ACS Nano)
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  2. ; ; ; ; ; ; (, Langmuir)
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  3. ; ; ; ; ; (, FDG '18: Proceedings of the 13th International Conference on the Foundations of Digital Game)
    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. 
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