General Video Game Playing

One of the grand challenges of AI is to create general intelligence: an agent that can excel at many tasks, not just one. In the area of games, this has given rise to the challenge of General Game Playing (GGP). In GGP, the game (typically a turn-taking board game) is defined declaratively in terms of the logic of the game (what happens when a move is made, how the scoring system works, how the winner is declared, and so on). The AI player then has to work out how to play the game and how to win. In this work, we seek to extend the idea of General Game Playing into the realm of video games, thus forming the area of General Video Game Playing (GVGP). In GVGP, computational agents will be asked to play video games that they have not seen before. At the minimum, the agent will be given the current state of the world and told what actions are applicable. Every game tick the agent will have to decide on its action, and the state will be updated, taking into account the actions of the other agents in the game and the game physics. We envisage running a competition based on GVGP playing, using arcadestyle (e.g. similar to Atari 2600) games as our starting point. These games are rich enough to be a formidable challenge to a GVGP agent, without introducing unnecessary complexity. The competition that we envisage could have a number of tracks, based on the form of the state (frame buffer or object model) and whether or not a forward model of action execution is available. We propose that the existing Physical Travelling Salesman (PTSP) software could be extended for our purposes and that a variety of GVGP games could be created in this framework by AI and Games students and other developers. Beyond this, we envisage the development of a Video Game Description Language (VGDL) as a way of concisely specifying video games. For the competition, we see this as being an interesting challenge in terms of deliberative search, machine learning and transfer of existing knowledge into new domains.

[1]  Richard Pawson,et al.  Robot control system for window cleaning , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[2]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[3]  Wolfram Burgard,et al.  MINERVA: a second-generation museum tour-guide robot , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[4]  Jonathan Schaeffer,et al.  Chips Challenging Champions: Games, Computers and Artificial Intelligence , 2002 .

[5]  Peter Norvig,et al.  Artificial intelligence - a modern approach, 2nd Edition , 2003, Prentice Hall series in artificial intelligence.

[6]  Marcus Hutter Universal Artificial Intellegence - Sequential Decisions Based on Algorithmic Probability , 2005, Texts in Theoretical Computer Science. An EATCS Series.

[7]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 2005, IEEE Transactions on Neural Networks.

[8]  Jesper Juul Half-Real: Video Games between Real Rules and Fictional Worlds , 2005 .

[9]  Michael R. Genesereth,et al.  General Game Playing: Overview of the AAAI Competition , 2005, AI Mag..

[10]  Csaba Szepesvári,et al.  Bandit Based Monte-Carlo Planning , 2006, ECML.

[11]  Simon M. Lucas Ms Pac-Man competition , 2007, SEVO.

[12]  Scott D. Goodwin,et al.  Knowledge Generation for Improving Simulations in UCT for General Game Playing , 2008, Australasian Conference on Artificial Intelligence.

[13]  Yngvi Björnsson,et al.  Simulation-Based Approach to General Game Playing , 2008, AAAI.

[14]  Yngvi Björnsson,et al.  CadiaPlayer: A Simulation-Based General Game Player , 2009, IEEE Transactions on Computational Intelligence and AI in Games.

[15]  Philip Hingston The 2K BotPrize , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[16]  Yavar Naddaf,et al.  Game-independent AI agents for playing Atari 2600 console games , 2010 .

[17]  Julian Togelius,et al.  Search-Based Procedural Content Generation , 2010, EvoApplications.

[18]  Marius Thomas Lindauer,et al.  Centurio, a General Game Player: Parallel, Java- and ASP-based , 2010, KI - Künstliche Intelligenz.

[19]  Jean Méhat,et al.  A Parallel General Game Player , 2010, KI - Künstliche Intelligenz.

[20]  Y. Hosoda,et al.  Autonomous Moving Technology for Future Urban Transport , 2011 .

[21]  Julian Togelius,et al.  Measuring Intelligence through Games , 2011, ArXiv.

[22]  Risto Miikkulainen,et al.  HyperNEAT-GGP: a hyperNEAT-based atari general game player , 2012, GECCO '12.

[23]  Marc G. Bellemare,et al.  Investigating Contingency Awareness Using Atari 2600 Games , 2012, AAAI.

[24]  Simon M. Lucas,et al.  A Survey of Monte Carlo Tree Search Methods , 2012, IEEE Transactions on Computational Intelligence and AI in Games.

[25]  Simon M. Lucas,et al.  Using genetic programming to evolve heuristics for a Monte Carlo Tree Search Ms Pac-Man agent , 2013, 2013 IEEE Conference on Computational Inteligence in Games (CIG).

[26]  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).

[27]  Moshe Sipper,et al.  EvoMCTS: Enhancing MCTS-based players through genetic programming , 2013, 2013 IEEE Conference on Computational Inteligence in Games (CIG).

[28]  Risto Miikkulainen,et al.  A Neuroevolution Approach to General Atari Game Playing , 2014, IEEE Transactions on Computational Intelligence and AI in Games.

[29]  Marc G. Bellemare,et al.  The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract) , 2012, IJCAI.