University of Hertfordshire

By the same authors

Deceptive Games

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Deceptive Games. / Anderson, Damien; Stephenson, Matthew; Togelius, Julian; Salge, Christoph; Levine, John; Renz, Jochen.

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 proceedingConference contribution

Harvard

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

APA

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

Vancouver

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)). https://doi.org/10.1007/978-3-319-77538-8_26

Author

Anderson, Damien ; Stephenson, Matthew ; Togelius, Julian ; Salge, Christoph ; Levine, John ; Renz, Jochen. / 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)).

Bibtex

@inproceedings{f87e096bec7f497c9d6c9b3bed9bae0f,
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",
note = "{\circledC} Springer International Publishing AG, part of Springer Nature 2018",
year = "2018",
month = "3",
day = "8",
doi = "10.1007/978-3-319-77538-8_26",
language = "English",
isbn = "9783319775371",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "376--391",
editor = "Kevin Sim and Paul Kaufmann",
booktitle = "Applications of Evolutionary Computation - 21st International Conference, EvoApplications 2018, Proceedings",
address = "Germany",

}

RIS

TY - GEN

T1 - Deceptive Games

AU - Anderson, Damien

AU - Stephenson, Matthew

AU - Togelius, Julian

AU - Salge, Christoph

AU - Levine, John

AU - Renz, Jochen

N1 - © Springer International Publishing AG, part of Springer Nature 2018

PY - 2018/3/8

Y1 - 2018/3/8

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.

KW - Deception

KW - Games

KW - Reinforcement learning

KW - Tree search

UR - http://www.scopus.com/inward/record.url?scp=85044062940&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-77538-8_26

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

M3 - Conference contribution

SN - 9783319775371

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

SP - 376

EP - 391

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

A2 - Sim, Kevin

A2 - Kaufmann, Paul

PB - Springer Verlag

ER -