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Evolving Chess-like Games Using Relative Algorithm Performance Profiles

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Applications of Evolutionary Computation (EvoApplications 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9597))

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Abstract

We deal with the problem of automatic generation of complete rules of an arbitrary game. This requires a generic and accurate evaluating function that is used to score games. Recently, the idea that game quality can be measured using differences in performance of various game-playing algorithms of different strengths has been proposed; this is called Relative Algorithm Performance Profiles.

We formalize this method into a generally application algorithm estimating game quality, according to some set of model games with properties that we want to reproduce. We applied our method to evolve chess-like boardgames. The results show that we can obtain playable and balanced games of high quality.

J. Kowalski—Supported in part by the National Science Centre, Poland under project number 2014/13/N/ST6/01817. M. Szykuła—Supported in part by the National Science Centre, Poland under project number 2013/09/N/ST6/01194.

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Authors and Affiliations

  1. Institute of Computer Science, University of Wrocław, Wrocław, Poland

    Jakub Kowalski & Marek Szykuła

Authors
  1. Jakub Kowalski
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  2. Marek Szykuła
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Corresponding author

Correspondence to Jakub Kowalski .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Aalborg University, Copenhagen, Denmark

    Paolo Burelli

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Kowalski, J., Szykuła, M. (2016). Evolving Chess-like Games Using Relative Algorithm Performance Profiles. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_37

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  • DOI: https://doi.org/10.1007/978-3-319-31204-0_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31203-3

  • Online ISBN: 978-3-319-31204-0

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