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Orthogonally Evolved AI to Improve Difficulty Adjustment in Video Games

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Applications of Evolutionary Computation (EvoApplications 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9597))

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Abstract

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 highly-evolved 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.

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Notes

  1. 1.

    Our study was exempt by the Office of Research Support at the University of Texas at Austin. Number 2013-09-0084. Due to the exemption, by not taking any personal data, and due to the anonymity of the subjects, we did not need written consent.

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Acknowledgments

We would like to thank Chris Adami for insightful comments and discussion of the project.

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Authors and Affiliations

  1. Michigan State University, Michigan, USA

    Arend Hintze

  2. University of Pennsylvania, Pennsylvania, USA

    Randal S. Olson

  3. IT University of Copenhagen, Copenhagen, Denmark

    Joel Lehman

Authors
  1. Arend Hintze
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  2. Randal S. Olson
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  3. Joel Lehman
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Corresponding author

Correspondence to Arend Hintze .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Aalborg University, Copenhagen, Denmark

    Paolo Burelli

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Hintze, A., Olson, R.S., Lehman, J. (2016). Orthogonally Evolved AI to Improve Difficulty Adjustment in Video Games. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_34

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  • DOI: https://doi.org/10.1007/978-3-319-31204-0_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31203-3

  • Online ISBN: 978-3-319-31204-0

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