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Orthogonally evolved AI to improve difficulty adjustment in video games
Michigan State University, East Lansing, United States.ORCID iD: 0000-0002-4872-1961
2016 (English)In: Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science, vol 9597 / [ed] Squillero G., Burelli P., Springer Verlag , 2016, Vol. 9597, p. 525-540Conference paper (Refereed)
Abstract [en]

Computer games are most engaging when their difficulty is well matched to the player’s ability, thereby providing an experience in which the player is neither overwhelmed nor bored. In games where the player interacts with computer-controlled opponents, the difficulty of the game can be adjusted not only by changing the distribution of opponents or game resources, but also through modifying the skill of the opponents. Applying evolutionary algorithms to evolve the artificial intelligence that controls opponent agents is one established method for adjusting opponent difficulty. Less-evolved agents (i.e., agents subject to fewer generations of evolution) make for easier opponents, while highlyevolved agents are more challenging to overcome. In this publication we test a new approach for difficulty adjustment in games: orthogonally evolved AI, where the player receives support from collaborating agents that are co-evolved with opponent agents (where collaborators and opponents have orthogonal incentives). The advantage is that game difficulty can be adjusted more granularly by manipulating two independent axes: by having more or less adept collaborators, and by having more or less adept opponents. Furthermore, human interaction can modulate (and be informed by) the performance and behavior of collaborating agents. In this way, orthogonally evolved AI both facilitates smoother difficulty adjustment and enables new game experiences. © Springer International Publishing Switzerland 2016.

Place, publisher, year, edition, pages
Springer Verlag , 2016. Vol. 9597, p. 525-540
Keywords [en]
Coevolution, Difficulty adjustment, Evolutionary computation, Markov networks, Artificial intelligence, Calculations, Evolutionary algorithms, Human computer interaction, Interactive computer graphics, Co-evolution, Collaborating agents, Game experience, Human interactions, New approaches, Opponent agent, Computer games
National Category
Computer Sciences Evolutionary Biology
Identifiers
URN: urn:nbn:se:du-37178DOI: 10.1007/978-3-319-31204-0_34Scopus ID: 2-s2.0-84961725746ISBN: 9783319312033 (print)OAI: oai:DiVA.org:du-37178DiVA, id: diva2:1557879
Conference
19th European Conference, EvoApplications 2016, Porto, Portugal, March 30-April 1, 2016
Available from: 2021-05-27 Created: 2021-05-27 Last updated: 2021-05-27Bibliographically approved

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Hintze, Arend

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
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