Deceptive Games

Damien Anderson; Matthew Stephenson; Julian Togelius; Christoph Salge; John Levine; Jochen Renz

doi:10.1007/978-3-319-77538-8_26

Deceptive Games

Damien Anderson^*, Matthew Stephenson, Julian Togelius, Christoph Salge, John Levine, Jochen Renz

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

20 Citations (Scopus)

Abstract

Deceptive games are games where the reward structure or other aspects of the game are designed to lead the agent away from a globally optimal policy. While many games are already deceptive to some extent, we designed a series of games in the Video Game Description Language (VGDL) implementing specific types of deception, classified by the cognitive biases they exploit. VGDL games can be run in the General Video Game Artificial Intelligence (GVGAI) Framework, making it possible to test a variety of existing AI agents that have been submitted to the GVGAI Competition on these deceptive games. Our results show that all tested agents are vulnerable to several kinds of deception, but that different agents have different weaknesses. This suggests that we can use deception to understand the capabilities of a game-playing algorithm, and game-playing algorithms to characterize the deception displayed by a game.

Original language	English
Title of host publication	Applications of Evolutionary Computation - 21st International Conference, EvoApplications 2018, Proceedings
Editors	Kevin Sim, Paul Kaufmann
Publisher	Springer Verlag
Pages	376-391
Number of pages	16
ISBN (Print)	9783319775371
DOIs	https://doi.org/10.1007/978-3-319-77538-8_26
Publication status	Published - 2018
Event	21st International Conference on Applications of Evolutionary Computation, EvoApplications 2018 - parma, Italy Duration: 4 Apr 2018 → 6 Apr 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10784 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Applications of Evolutionary Computation, EvoApplications 2018
Country/Territory	Italy
City	parma
Period	4/04/18 → 6/04/18

Access to Document

10.1007/978-3-319-77538-8_26

Cite this

Anderson, D., Stephenson, M., Togelius, J., Salge, C., Levine, J., & Renz, J. (2018). Deceptive Games. In K. Sim, & P. Kaufmann (Eds.), Applications of Evolutionary Computation - 21st International Conference, EvoApplications 2018, Proceedings (pp. 376-391). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10784 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-77538-8_26

Anderson, Damien ; Stephenson, Matthew ; Togelius, Julian et al. / Deceptive Games. Applications of Evolutionary Computation - 21st International Conference, EvoApplications 2018, Proceedings. editor / Kevin Sim ; Paul Kaufmann. Springer Verlag, 2018. pp. 376-391 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{89203cf9f4164fc8b6c643226ab5581e,

title = "Deceptive Games",

abstract = "Deceptive games are games where the reward structure or other aspects of the game are designed to lead the agent away from a globally optimal policy. While many games are already deceptive to some extent, we designed a series of games in the Video Game Description Language (VGDL) implementing specific types of deception, classified by the cognitive biases they exploit. VGDL games can be run in the General Video Game Artificial Intelligence (GVGAI) Framework, making it possible to test a variety of existing AI agents that have been submitted to the GVGAI Competition on these deceptive games. Our results show that all tested agents are vulnerable to several kinds of deception, but that different agents have different weaknesses. This suggests that we can use deception to understand the capabilities of a game-playing algorithm, and game-playing algorithms to characterize the deception displayed by a game.",

keywords = "Deception, Games, Reinforcement learning, Tree search",

author = "Damien Anderson and Matthew Stephenson and Julian Togelius and Christoph Salge and John Levine and Jochen Renz",

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Anderson, D, Stephenson, M, Togelius, J, Salge, C, Levine, J & Renz, J 2018, Deceptive Games. in K Sim & P Kaufmann (eds), Applications of Evolutionary Computation - 21st International Conference, EvoApplications 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10784 LNCS, Springer Verlag, pp. 376-391, 21st International Conference on Applications of Evolutionary Computation, EvoApplications 2018, parma, Italy, 4/04/18. https://doi.org/10.1007/978-3-319-77538-8_26

Deceptive Games. / Anderson, Damien; Stephenson, Matthew; Togelius, Julian et al.
Applications of Evolutionary Computation - 21st International Conference, EvoApplications 2018, Proceedings. ed. / Kevin Sim; Paul Kaufmann. Springer Verlag, 2018. p. 376-391 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10784 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deceptive Games

AU - Anderson, Damien

AU - Stephenson, Matthew

AU - Togelius, Julian

AU - Salge, Christoph

AU - Levine, John

AU - Renz, Jochen

N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.

PY - 2018

Y1 - 2018

N2 - Deceptive games are games where the reward structure or other aspects of the game are designed to lead the agent away from a globally optimal policy. While many games are already deceptive to some extent, we designed a series of games in the Video Game Description Language (VGDL) implementing specific types of deception, classified by the cognitive biases they exploit. VGDL games can be run in the General Video Game Artificial Intelligence (GVGAI) Framework, making it possible to test a variety of existing AI agents that have been submitted to the GVGAI Competition on these deceptive games. Our results show that all tested agents are vulnerable to several kinds of deception, but that different agents have different weaknesses. This suggests that we can use deception to understand the capabilities of a game-playing algorithm, and game-playing algorithms to characterize the deception displayed by a game.

AB - Deceptive games are games where the reward structure or other aspects of the game are designed to lead the agent away from a globally optimal policy. While many games are already deceptive to some extent, we designed a series of games in the Video Game Description Language (VGDL) implementing specific types of deception, classified by the cognitive biases they exploit. VGDL games can be run in the General Video Game Artificial Intelligence (GVGAI) Framework, making it possible to test a variety of existing AI agents that have been submitted to the GVGAI Competition on these deceptive games. Our results show that all tested agents are vulnerable to several kinds of deception, but that different agents have different weaknesses. This suggests that we can use deception to understand the capabilities of a game-playing algorithm, and game-playing algorithms to characterize the deception displayed by a game.

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DO - 10.1007/978-3-319-77538-8_26

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Applications of Evolutionary Computation - 21st International Conference, EvoApplications 2018, Proceedings

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ER -

Anderson D, Stephenson M, Togelius J, Salge C, Levine J, Renz J. Deceptive Games. In Sim K, Kaufmann P, editors, Applications of Evolutionary Computation - 21st International Conference, EvoApplications 2018, Proceedings. Springer Verlag. 2018. p. 376-391. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-77538-8_26

Deceptive Games

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