Deceptive Games

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.

[1]  Tom Schaul,et al.  Unifying Count-Based Exploration and Intrinsic Motivation , 2016, NIPS.

[2]  Omar Syed,et al.  ARIMAA - A NEW GAME DESIGNED TO BE DIFFICULT FOR COMPUTERS , 2003 .

[3]  Julian Togelius,et al.  Artificial Intelligence and Games , 2018, Springer International Publishing.

[4]  L. Darrell Whitley,et al.  Fundamental Principles of Deception in Genetic Search , 1990, FOGA.

[5]  Julian Togelius,et al.  Towards generating arcade game rules with VGDL , 2015, 2015 IEEE Conference on Computational Intelligence and Games (CIG).

[6]  Julian Togelius,et al.  Towards a Video Game Description Language , 2013, Artificial and Computational Intelligence in Games.

[7]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[8]  Murray Campbell,et al.  Deep Blue , 2002, Artif. Intell..

[9]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[10]  Julian Togelius,et al.  Hyper-heuristic general video game playing , 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).

[11]  Julian Togelius,et al.  Analyzing the robustness of general video game playing agents , 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).

[12]  E. Rowland Theory of Games and Economic Behavior , 1946, Nature.

[13]  B. Newell Judgment Under Uncertainty , 2013 .

[14]  Tom Schaul,et al.  A video game description language for model-based or interactive learning , 2013, 2013 IEEE Conference on Computational Inteligence in Games (CIG).

[15]  Julian Togelius,et al.  General Video Game AI: Competition, Challenges and Opportunities , 2016, AAAI.

[16]  G Gigerenzer,et al.  Reasoning the fast and frugal way: models of bounded rationality. , 1996, Psychological review.

[17]  Alexei A. Efros,et al.  Curiosity-Driven Exploration by Self-Supervised Prediction , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[18]  Mark J. Nelson,et al.  Investigating vanilla MCTS scaling on the GVG-AI game corpus , 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).

[19]  Steve Coles Chess , 1973, SGAR.

[20]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[21]  Julian Togelius,et al.  Matching Games and Algorithms for General Video Game Playing , 2021, AIIDE.

[22]  Julian Togelius,et al.  Ieee Transactions on Computational Intelligence and Ai in Games the 2014 General Video Game Playing Competition , 2022 .

[23]  Kalyanmoy Deb,et al.  Analyzing Deception in Trap Functions , 1992, FOGA.