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General Game PlayingApril 2014
Publisher:
  • Morgan & Claypool Publishers
ISBN:978-1-62705-255-9
Published:01 April 2014
Pages:
230
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Abstract

General game players are computer systems able to play strategy games based solely on formal game descriptions supplied at "runtime" (n other words, they don't know the rules until the game starts). Unlike specialized game players, such as Deep Blue, general game players cannot rely on algorithms designed in advance for specific games; they must discover such algorithms themselves. General game playing expertise depends on intelligence on the part of the game player and not just intelligence of the programmer of the game player. GGP is an interesting application in its own right. It is intellectually engaging and more than a little fun. But it is much more than that. It provides a theoretical framework for modeling discrete dynamic systems and defining rationality in a way that takes into account problem representation and complexities like incompleteness of information and resource bounds. It has practical applications in areas where these features are important, e.g., in business and law. More fundamentally, it raises questions about the nature of intelligence and serves as a laboratory in which to evaluate competing approaches to artificial intelligence. This book is an elementary introduction to General Game Playing (GGP). (1) It presents the theory of General Game Playing and leading GGP technologies. (2) It shows how to create GGP programs capable of competing against other programs and humans. (3) It offers a glimpse of some of the real-world applications of General Game Playing.

Cited By

  1. Cunanan M and Thielscher M On optimal strategies for wordle and general guessing games Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, (5541-5548)
  2. Sironi C and Winands M (2021). Analysis of the Impact of Randomization of Search-Control Parameters in Monte-Carlo Tree Search, Journal of Artificial Intelligence Research, 72, (717-757), Online publication date: 4-Jan-2022.
  3. Gros T, Groß J and Wolf V Real-time decision making for a car manufacturing process using deep reinforcement learning Proceedings of the Winter Simulation Conference, (3032-3044)
  4. Cropper A, Evans R and Law M (2019). Inductive general game playing, Machine Language, 109:7, (1393-1434), Online publication date: 1-Jul-2020.
  5. Hernández-Orallo J (2019). AI generality and spearman's law of diminishing returns, Journal of Artificial Intelligence Research, 64:1, (529-562), Online publication date: 1-Jan-2019.
  6. Engesser T, Mattmüller R, Nebel B and Thielscher M Game description language and dynamic epistemic logic compared Proceedings of the 27th International Joint Conference on Artificial Intelligence, (1795-1802)
  7. Kostka B, Kwiecieli J, Kowalski J and Rychlikowski P Text-based adventures of the golovin AI agent 2017 IEEE Conference on Computational Intelligence and Games (CIG), (181-188)
  8. Thielscher M GDL-III Proceedings of the 26th International Joint Conference on Artificial Intelligence, (1276-1282)
  9. de Jonge D and Zhang D Lifted Backward Search for General Game Playing AI 2016: Advances in Artificial Intelligence, (3-16)
  10. Hernandez-Orallo J Is Spearman's law of diminishing returns (SLODR) meaningful for artificial agents? Proceedings of the Twenty-second European Conference on Artificial Intelligence, (471-479)
  11. Thielscher M GDL-III Proceedings of the Twenty-second European Conference on Artificial Intelligence, (1630-1631)
  12. Thielscher M Simulation of Action Theories and an Application to General Game-Playing Robots Essays Dedicated to Gerhard Brewka on the Occasion of His 60th Birthday on Advances in Knowledge Representation, Logic Programming, and Abstract Argumentation - Volume 9060, (33-46)
Contributors
  • Stanford University
  • UNSW Sydney

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