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Heuristic Search for Tetris: A Case Study

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Intelligent Computing (CompCom 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 997))

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Abstract

Games represent important benchmark problems for AI. One-player games, also called puzzles, often resemble real world optimization problems and, thus, lessons learned on such games are also important for such problems. In this paper we focus on the game of Tetris, which can also be seen as a packing problem variant. We provide an empirical evaluation of a heuristic search approach for Tetris with the following goal: Having an effective heuristic function at hand, we want to answer the question how much the additional tree search pays off. We are especially interested if there is a so called sweet spot that represents the best ratio between score achieved and time invested in the search. This knowledge is crucial in order to be able to implement deep-learning approaches in the light of limited computing resources, i.e. to produce many good games to be learned from in rather short time. Our experiments reveal that such a sweet spot exists and hence, using this knowledge, only a fraction of time is needed for producing the same amount of learning data with similar quality.

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Acknowledgement

Work has been conducted in the scope of the research project Productive4.0 (H2020-ECSEL-GANo.: 737459).

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Authors and Affiliations

  1. Alpen-Adria Universität Klagenfurt, 9020, Klagenfurt, Austria

    Giacomo Da Col & Erich C. Teppan

Authors
  1. Giacomo Da Col
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  2. Erich C. Teppan
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Corresponding author

Correspondence to Giacomo Da Col .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information SAI Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information SAI Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Da Col, G., Teppan, E.C. (2019). Heuristic Search for Tetris: A Case Study. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Intelligent Computing. CompCom 2019. Advances in Intelligent Systems and Computing, vol 997. Springer, Cham. https://doi.org/10.1007/978-3-030-22871-2_28

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