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Towards a hybrid neural and evolutionary heuristic approach for playing tile-matching puzzle games
Malmö högskola, Faculty of Technology and Society (TS).ORCID iD: 0000-0003-3924-7484
2017 (English)In: Proceedings of the 2017 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, 2017, p. 76-79Conference paper, Published paper (Refereed)
Abstract [en]

Abstract: In this paper we explore the hybrid application of evolutionary computation and artificial neural networks in the development of intelligent systems able to solve the problem of approximating the optimal strategy in a tile-matching puzzle game. Three intelligent systems are proposed: an evolutionary heuristic technique, artificial neural networks, and a hybrid approach that combines both. Results show that the hybrid approach, which combines the advantages of the two previous solutions, performs better at both, the number of completed lines and the average piece placement time. These results aim to serve as the basis for a later comparative study against state- of-the-art techniques in the topic.

Place, publisher, year, edition, pages
IEEE, 2017. p. 76-79
Keywords [en]
Games, Neural Networks, Genetic Algorithms
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mau:diva-12569DOI: 10.1109/CIG.2017.8080418Scopus ID: 2-s2.0-85040008170Local ID: 24153OAI: oai:DiVA.org:mau-12569DiVA, id: diva2:1409616
Conference
2017 IEEE Conference on Computational Intelligence and Games (CIG), New York, USA (22-25 August, 2017)
Available from: 2020-02-29 Created: 2020-02-29 Last updated: 2024-02-05Bibliographically approved

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Publisher's full textScopushttp://www.cig2017.com/

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Font, Jose M

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf