General game playing

General game playing (GGP) is the design of artificial intelligence programs to be able to play more than one game successfully.[1][2][3] For many games like chess, computers are programmed to play these games using a specially designed algorithm, which cannot be transferred to another context. For instance, a chess-playing computer program cannot play checkers. General game playing is considered as a necessary milestone on the way to Artificial General Intelligence.[4]

General video game playing (GVGP) is the concept of GGP adjusted to the purpose of playing video games. For video games, game rules have to be either learnt over multiple iterations by artificial players like TD-Gammon,[5] or are predefined manually in a domain-specific language and sent in advance to artificial players[6][7] like in traditional GGP. Starting in 2013, significant progress was made following the deep reinforcement learning approach, including the development of programs that can learn to play Atari 2600 games[8][5][9][10][11] as well as a program that can learn to play Nintendo Entertainment System games.[12][13][14]

The first commercial usage of general game playing technology was Zillions of Games in 1998. General game playing was also proposed for trading agents in supply chain management thereunder price negotiation in online auctions from 2003 on.[15][16][17][18]

History

In 1992, Barney Pell defined the concept of Meta-Game Playing, and developed the "MetaGame" system. This was the first program to automatically generate game rules of chess-like games, and one of the earliest programs to use automated game generation. Pell then developed the system Metagamer.[19] This system was able to play a number of chess-like games, given game rules definition in a special language called Game Description Language (GDL), without any human interaction once the games were generated.[20]

In 1998, the commercial system Zillions of Games was developed by Jeff Mallett and Mark Lefler. The system used a LISP-like language to define the game rules. Zillions of Games derived the evaluation function automatically from the game rules based on piece mobility, board structure and game goals. It also employed usual algorithms as found in computer chess systems: alpha-beta pruning with move ordering, transposition tables, etc.[21] The package was extended in 2007 by the addition of the Axiom plug-in, an alternate metagame engine that incorporates a complete Forth-based programming language.

In 1998, z-Tree was developed by Urs Fischbacher.[22] z-Tree is the first and the most cited software tool for experimental economics. z-Tree allows the definition of game rules in z-Tree-language for game-theoretic experiments with human subjects. It also allows definition of computer players, which participate in a play with human subjects.[23]

In 2005, the Stanford Project General Game Playing was established.[3]

In 2012, the development of PyVGDL started.[24]

GGP implementations

Stanford project

General Game Playing is a project of the Stanford Logic Group of Stanford University, California, which aims to create a platform for general game playing. It is the most well-known effort at standardizing GGP AI, and generally seen as the standard for GGP systems. The games are defined by sets of rules represented in the Game Description Language. In order to play the games, players interact with a game hosting server[25][26] that monitors moves for legality and keeps players informed of state changes.

Since 2005, there have been annual General Game Playing competitions at the AAAI Conference. The competition judges competitor AI's abilities to play a variety of different games, by recording their performance on each individual game. In the first stage of the competition, entrants are judged on their ability to perform legal moves, gain the upper hand, and complete games faster. In the following runoff round, the AIs face off against each other in increasingly complex games. The AI that wins the most games at this stage wins the competition, and until 2013 its creator used to win a $10,000 prize.[19] So far, the following programs were victorious:[27]

Other approaches

There are other general game playing systems, which use their own languages for defining the game rules. Other general game playing software include:

  • A system called AiAi by Stephen Tavener (previous Zillions developer)[31] going back to 2015 and actively developed as of late 2017.[32][33]
  • A system called the PolyGamo Player by David M. Bennett released in September 2017 based on the Unity game engine.[34]

GVGP implementations

Reinforcement learning

GVGP could potentially be used to create real video game AI automatically, as well as "to test game environments, including those created automatically using procedural content generation and to find potential loopholes in the gameplay that a human player could exploit".[7] GVGP has also been used to generate game rules, and estimate a game's quality based on Relative Algorithm Performance Profiles (RAPP), which compare the skill differentiation that a game allows between good AI and bad AI.[35]

Video Game Description Language

The General Video Game AI Competition (GVGAI) has been running since 2014. In this competition, two-dimensional video games similar to (and sometimes based on) 1980s-era arcade and console games are used instead of the board games used in the GGP competition. It has offered a way for researchers and practitioners to test and compare their best general video game playing algorithms. The competition has an associated software framework including a large number of games written in the Video Game Description Language (VGDL), which should not be confused with GDL and is a coding language using simple semantics and commands that can easily be parsed. One example for VGDL is PyVGDL developed in 2013.[6][24] The games used in GVGP are, for now, often 2-dimensional arcade games, as they are the simplest and easiest to quantify.[36] To simplify the process of creating an AI that can interpret video games, games for this purpose are written in VGDL manually. VGDL can be used to describe a game specifically for procedural generation of levels, using Answer Set Programming (ASP) and an Evolutionary Algorithm (EA). GVGP can then be used to test the validity of procedural levels, as well as the difficulty or quality of levels based on how an agent performed.[37]

Algorithms

Since GGP AI must be designed to play multiple games, its design cannot rely on algorithms created specifically for certain games. Instead, the AI must be designed using algorithms whose methods can be applied to a wide range of games. The AI must also be an ongoing process, that can adapt to its current state rather than the output of previous states. For this reason, open loop techniques are often most effective.[38]

A popular method for developing GGP AI is the Monte Carlo tree search (MCTS) algorithm.[39] Often used together with the UCT method (Upper Confidence Bound applied to Trees), variations of MCTS have been proposed to better play certain games, as well as to make it compatible with video game playing.[40][41][42] Another variation of tree-search algorithms used is the Directed Breadth-first Search (DBS),[43] in which a child node to the current state is created for each available action, and visits each child ordered by highest average reward, until either the game ends or runs out of time.[44] In each tree-search method, the AI simulates potential actions and ranks each based on the average highest reward of each path, in terms of points earned.[39][44]

Assumptions

In order to interact with games, algorithms must operate under the assumption that games all share common characteristics. In the book Half-Real: Video Games Between Real Worlds and Fictional Worlds, Jesper Juul gives the following definition of games: Games are based on rules, they have variable outcomes, different outcomes give different values, player effort influences outcomes, the player is attached to the outcomes, and the game has negotiable consequences.[45] Using these assumptions, game playing AI can be created by quantifying the player input, the game outcomes, and how the various rules apply, and using algorithms to compute the most favorable path.[36]

See also

References
  1. Pell, Barney (1992). H. van den Herik; L. Allis (eds.). "Metagame: a new challenge for games and learning" [Heuristic programming in artificial intelligence 3–the third computerolympiad] (PDF). Ellis-Horwood. Cite journal requires |journal= (help)
  2. Pell, Barney (1996). "A Strategic Metagame Player for General Chess-Like Games". Computational Intelligence. 12 (1): 177–198. doi:10.1111/j.1467-8640.1996.tb00258.x. ISSN 1467-8640.
  3. Genesereth, Michael; Love, Nathaniel; Pell, Barney (15 June 2005). "General Game Playing: Overview of the AAAI Competition". AI Magazine. 26 (2): 62. doi:10.1609/aimag.v26i2.1813. ISSN 2371-9621.
  4. Canaan, Rodrigo; Salge, Christoph; Togelius, Julian; Nealen, Andy (2019). Leveling the playing field [Proceedings of the 14th International Conference on theLeveling the playing field: fairness in AI versus human game benchmarks]. dl.acm.org. pp. 1–8. doi:10.1145/3337722. ISBN 9781450372176.
  5. Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Graves, Alex; Antonoglou, Ioannis; Wierstra, Daan; Riedmiller, Martin (2013). "Playing Atari with Deep Reinforcement Learning" (PDF). Neural Information Processing Systems Workshop 2013. Retrieved 25 April 2015.
  6. Schaul, Tom (August 2013). "A video game description language for model-based or interactive learning". 2013 IEEE Conference on Computational Intelligence in Games (CIG): 1–8. CiteSeerX 10.1.1.360.2263. doi:10.1109/CIG.2013.6633610. ISBN 978-1-4673-5311-3.
  7. Levine, John; Congdon, Clare Bates; Ebner, Marc; Kendall, Graham; Lucas, Simon M.; Miikkulainen, Risto; Schaul, Tom; Thompson, Tommy (2013). "General Video Game Playing". Artificial and Computational Intelligence in Games. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik. 6: 77–83. Retrieved 25 April 2015.
  8. Bowling, M.; Veness, J.; Naddaf, Y.; Bellemare, M. G. (2013-06-14). "The Arcade Learning Environment: An Evaluation Platform for General Agents". Journal of Artificial Intelligence Research. 47: 253–279. arXiv:1207.4708. doi:10.1613/jair.3912. ISSN 1076-9757.
  9. Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Hassabis, Demis; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Stig Petersen, Georg Ostrovski; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane (26 February 2015). "Human-level control through deep reinforcement learning". Nature. 518 (7540): 529–533. Bibcode:2015Natur.518..529M. doi:10.1038/nature14236. PMID 25719670.
  10. Korjus, Kristjan; Kuzovkin, Ilya; Tampuu, Ardi; Pungas, Taivo (2014). "Replicating the Paper "Playing Atari with Deep Reinforcement Learning"" (PDF). University of Tartu. Retrieved 25 April 2015.
  11. Guo, Xiaoxiao; Singh, Satinder; Lee, Honglak; Lewis, Richard L.; Wang, Xiaoshi (2014). "Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning" (PDF). NIPS Proceedingsβ. Conference on Neural Information Processing Systems. Retrieved 25 April 2015.
  12. Murphy, Tom (2013). "The First Level of Super Mario Bros. is Easy with Lexicographic Orderings and Time Travel ... after that it gets a little tricky." (PDF). SIGBOVIK. Retrieved 25 April 2015.
  13. Murphy, Tom. "learnfun & playfun: A general technique for automating NES games". Retrieved 25 April 2015.
  14. Teller, Swizec (October 28, 2013). "Week 2: Level 1 of Super Mario Bros. is easy with lexicographic orderings and". A geek with a hat. Retrieved 25 April 2015.
  15. McMillen, Colin (2003). "Toward the Development of an Intelligent Agent for the Supply Chain Management Game of the 2003 Trading Agent Competition" [2003 Trading Agent Competition]. Master's Thesis. Minneapolis, MN: University of Minnesota. S2CID 167336006. Cite journal requires |journal= (help)
  16. Zhang, Dongmo (2009). From general game descriptions to a market specification language for general trading agents [Agent-mediated electronic commerce. Designing trading strategies and mechanisms for electronic markets.]. Berlin, Heidelberg: Springer. pp. 259–274. Bibcode:2010aecd.book..259T. CiteSeerX 10.1.1.467.4629.
  17. "AGAPE - An Auction LanGuage for GenerAl Auction PlayErs". AGAPE (in French). Retrieved 5 March 2020.
  18. Michael, Friedrich; Ignatov, Dmitry (2019). "General Game Playing B-to-B Price Negotiations" (PDF). CEUR Workshop Proceedings. Vol-2479: 89–99. Retrieved 5 March 2020.
  19. Barney Pell's research on computer game playing Archived 2007-08-12 at the Wayback Machine.
  20. "Metagame and General Game Playing". Metagame and General Game Playing. Retrieved 27 March 2016.
  21. Available: Universal Game Engine email to comp.ai.games by Jeff Mallett, 10-Dec-1998.
  22. "UZH - z-Tree - Zurich Toolbox for Readymade Economic Experiments". www.ztree.uzh.ch. Retrieved 17 February 2020.
  23. Beckenkamp, Martin; Hennig‐Schmidt, Heike; Maier-Rigaud, Frank P. (1 March 2007). "Cooperation in Symmetric and Asymmetric Prisoner's Dilemma Games". Social Science Research Network. SSRN 968942. Cite journal requires |journal= (help)
  24. Schaul, Tom (7 February 2020). "schaul/py-vgdl".
  25. GGP Server, platform for competition of general game playing systems.
  26. Dresden GGP Server, platform for competition of general game playing systems with automatic scheduling of matches.
  27. "General Game Playing". www.general-game-playing.de.
  28. Information about Fluxplayer, the winner of the 2nd International General Game Playing competition.
  29. Information about CADIAPlayer, more information about the winner of the 3rd, 4th, and 8th International General Game Playing competitions.
  30. Sancho is GGP Champion 2014!, winner of the 2014 International General Game Playing competition.
  31. "Zillions of Games - Who Are We?". www.zillions-of-games.com. Retrieved 2017-11-16.
  32. "AiAi Home Page – Stephen Tavener". mrraow.com. Retrieved 2017-11-16.
  33. "Ai Ai announcement thread". BoardGameGeek. Retrieved 2017-11-16.
  34. "The PolyGamo Player Project | Programming Languages and General Players for Abstract Games and Puzzles". www.polyomino.com. Retrieved 2017-11-16.
  35. Nielsen, Thorbjørn S.; Barros, Gabriella A. B.; Togelius, Julian; Nelson, Mark J. "Towards generating arcade game rules with VGDL" (PDF).
  36. Levine, John; Congdon, Clare Bates; Ebner, Marc; Kendall, Graham; Lucas, Simon M.; Miikkulainen Risto, Schaul; Tom, Thompson; Tommy. "General Video Game Playing" (PDF).
  37. Neufeld, Xenija; Mostaghim, Sanaz; Perez-Liebana, Diego. "Procedural Level Generation with Answer Set Programming for General Video Game Playing" (PDF).
  38. "Recent Advances in General Game Playing". The Scientific World Journal. Hindawi Publishing Corporation. 2015. doi:10.1155/2015/986262.
  39. "Monte-Carlo Tree Search for General Game Playing". ResearchGate. Retrieved 2016-04-01.
  40. Finnsson, Hilmar (2012). "Generalized Monte-Carlo Tree Search Extensions for General Game Playing". Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence.
  41. Frydenberg, Frederik; Anderson, Kasper R.; Risi, Sebastian; Togelius, Julian. "Investigating MCTS Modifications in General Video Game Playing" (PDF).
  42. M. Swiechowski; J. Mandziuk; Y. S. Ong, "Specialization of a UCT-based General Game Playing Program to Single-Player Games," in IEEE Transactions on Computational Intelligence and AI in Games, vol.PP, no.99, pp.1-1 doi:10.1109/TCIAIG.2015.2391232
  43. "Changing the root node from a previous game step". DBS: A Directed Breadth First Search (DBS) algorithm
  44. Perez, Diego; Dieskau, Jens; Hünermund, Martin. "Open Loop Search for General Video Game Playing" (PDF).
  45. Jesper Juul. Half-Real: Video Games Between Real Rules and Fictional Worlds. MIT Press, 2005.
See also the CS227B - General Game Playing course page, GGP.org, GGP.org GitHub page, and games.stanford.edu.

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