Deceptive Games

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

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

11 Citations (Scopus)


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 languageEnglish
Title of host publicationApplications of Evolutionary Computation
Subtitle of host publication21st International Conference, EvoApplications 2018, Parma, Italy, April 4-6, 2018, Proceedings
EditorsKevin Sim, Paul Kaufmann
PublisherSpringer Nature
Number of pages16
ISBN (Electronic)9783319775388
ISBN (Print)9783319775371
Publication statusE-pub ahead of print - 8 Mar 2018
Event21st International Conference on Applications of Evolutionary Computation, EvoApplications 2018 - parma, Italy
Duration: 4 Apr 20186 Apr 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10784 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st International Conference on Applications of Evolutionary Computation, EvoApplications 2018


  • Deception
  • Games
  • Reinforcement learning
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