2026-07-22T21:19:21Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:4334 2024-03-06T10:28:02Z ddc:004 open_access

Learning and Game AI Muñoz-Avila, Hector Bauckhage, Christian Bida, Michal Congdon, Clare Bates Kendall, Graham Games machine learning artificial intelligence computational intelligence 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Hector Muñoz-Avila and Christian Bauckhage and Michal Bida and Clare Bates Congdon and Graham Kendall 2013 Is Part Of Dagstuhl Follow-Ups, Volume 6, Artificial and Computational Intelligence in Games (2013) InCollection Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DFU.Vol6.12191.33 urn:nbn:de:0030-drops-43348 https://drops.dagstuhl.de/entities/document/10.4230/DFU.Vol6.12191.33 eng https://creativecommons.org/licenses/by/3.0/legalcode