Evolving Chess-like Games Using Relative Algorithm Performance Profiles

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9597)

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.

Keywords

Procedural content generation Evolutionary algorithms Relative algorithm performance profiles Simplified board games General game playing 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of WrocławWrocławPoland

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