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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2019: Artificial Intelligence and Soft Computing pp 481–492Cite as

Strategizing Game Playing Using Evolutionary Approach

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11508))

Abstract

Since the inception of evolutionary algorithms, the capabilities of genetic algorithms is showcased by games. This paper proposes the use of a genetic algorithm for the game, Tetris. An evolutionary approach is used to design a Tetris bot. The proposed approach uses a novel set of parameters to decide which move needs to be taken by the Tetris bot for each falling Tetromino. These parameters represent the various genes present in the chromosome. Each individual is being allowed to play the game once. Once the entire population has played, the population undergoes crossover and mutation. In this way, the parameters are evolved to get a better bot. The most evolved bot as per the fitness is allowed to simulate 200 rounds of Tetris during which its actions are recorded. Further, Frequent Pattern Growth algorithm, a data mining technique, is used to extract knowledge from the given stored actions. The extracted knowledge is used for mining association rules and identifying strategies used by the evolved bot to play the game.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

    Abhinav Nagpal

  2. Department of Computer Science and Engineering, BML Munjal University, Gurugram, India

    Goldie Gabrani

Authors
  1. Abhinav Nagpal
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  2. Goldie Gabrani
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Corresponding author

Correspondence to Abhinav Nagpal .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Nagpal, A., Gabrani, G. (2019). Strategizing Game Playing Using Evolutionary Approach. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11508. Springer, Cham. https://doi.org/10.1007/978-3-030-20912-4_44

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  • DOI: https://doi.org/10.1007/978-3-030-20912-4_44

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20911-7

  • Online ISBN: 978-3-030-20912-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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