@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 = "{\textcopyright} Springer International Publishing AG, part of Springer Nature 2018; 21st International Conference on Applications of Evolutionary Computation, EvoApplications 2018 ; Conference date: 04-04-2018 Through 06-04-2018",
year = "2018",
month = mar,
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 Nature Link",
pages = "376--391",
editor = "Kevin Sim and Paul Kaufmann",
booktitle = "Applications of Evolutionary Computation",
address = "Netherlands",
}