Learning and Game AI

Muñoz-Avila, Hector; Bauckhage, Christian; Bida, Michal; Congdon, Clare Bates; Kendall, Graham

doi:10.4230/DFU.Vol6.12191.33

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Learning and Game AI

Authors Hector Muñoz-Avila, Christian Bauckhage, Michal Bida, Clare Bates Congdon, Graham Kendall

Part of: Volume: Artificial and Computational Intelligence in Games (DFU - Vol. 6)
Part of: Series: Dagstuhl Follow-Ups (DFU)
License: Creative Commons Attribution 3.0 Unported license
Publication Date: 2013-11-18

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DFU.Vol6.12191.33.pdf

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Document Identifiers

DOI: 10.4230/DFU.Vol6.12191.33
URN: urn:nbn:de:0030-drops-43348

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Keywords

Games
machine learning
artificial intelligence
computational intelligence

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Abstract

The incorporation of learning into commercial games can enrich the player experience, but may concern developers in terms of issues such as losing control of their game world. We explore a number of applied research and some fielded applications that point to the tremendous possibilities of machine learning research including game genres such as real-time strategy games, flight simulation games, car and motorcycle racing games, board games such as Go, an even traditional
game-theoretic problems such as the prisoners dilemma. A common trait of these works is the potential of machine learning to reduce the burden of game developers. However a number of challenges exists that hinder the use of machine learning more broadly. We discuss some of these challenges while at the same time exploring opportunities for a wide use of machine learning in games.

Cite As Get BibTex

Hector Muñoz-Avila, Christian Bauckhage, Michal Bida, Clare Bates Congdon, and Graham Kendall. Learning and Game AI. In Artificial and Computational Intelligence in Games. Dagstuhl Follow-Ups, Volume 6, pp. 33-43, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013) https://doi.org/10.4230/DFU.Vol6.12191.33

Author Details

Hector Muñoz-Avila

Christian Bauckhage

Michal Bida

Clare Bates Congdon

Graham Kendall

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