Back Algemene speletjie speel (KI) Afrikaans لعب لعبة عامة Arabic General game playing Czech General Game Playing German بازی کردن عمومی Persian عمومي لوبېدل Pashto/Pushto Универсальная игровая программа Russian Genel oyun oynama Turkish 瓣瓣掂玩遊戲 ZH-YUE

General game playing

Part of a series on
Artificial intelligence
Major goals
Approaches
Applications
Philosophy
History
Glossary

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 there under price negotiation in online auctions from 2003 on.[15][16][17][18]

  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. Archived (PDF) from the original on 2020-02-17. Retrieved 2020-02-17.
  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. S2CID 996006.
  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). Proceedings of the 14th International Conference on the Foundations of Digital Games [Proceedings of the 14th International Conference on the Leveling the playing field: fairness in AI versus human game benchmarks]. pp. 1–8. doi:10.1145/3337722. ISBN 9781450372176. S2CID 58599284.
  5. ^ a b 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. Archived (PDF) from the original on 12 September 2014. 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 Inteligence in Games (CIG). pp. 1–8. CiteSeerX 10.1.1.360.2263. doi:10.1109/CIG.2013.6633610. ISBN 978-1-4673-5311-3. S2CID 812565.
  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. 6. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik: 77–83. Archived from the original on 9 April 2016. 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. S2CID 1552061.
  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. S2CID 205242740.
  10. ^ Korjus, Kristjan; Kuzovkin, Ilya; Tampuu, Ardi; Pungas, Taivo (2014). "Replicating the Paper "Playing Atari with Deep Reinforcement Learning"" (PDF). University of Tartu. Archived (PDF) from the original on 18 December 2014. 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. Archived (PDF) from the original on 17 November 2015. 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. Archived (PDF) from the original on 26 April 2013. Retrieved 25 April 2015.
  13. ^ Murphy, Tom. "learnfun & playfun: A general technique for automating NES games". Archived from the original on 19 April 2015. 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. Archived from the original on 30 April 2015. 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] (Thesis). Master's Thesis. Minneapolis, MN: University of Minnesota. S2CID 167336006.
  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). 8 March 2019. Archived from the original on 2 August 2021. Retrieved 5 March 2020.
  18. ^ Michael, Friedrich; Ignatov, Dmitry (2019). "General Game Playing B-to-B Price Negotiations" (PDF). CEUR Workshop Proceedings. -2479: 89–99. Archived (PDF) from the original on 6 December 2019. Retrieved 5 March 2020.

Developed by StudentB