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General Video Game Artificial Intelligence [electronic resource] / by Diego Pérez Liébana, Simon M. Lucas, Raluca D. Gaina, Julian Togelius, Ahmed Khalifa, Jialin Liu.

By: Liébana, Diego Pérez [author.].
Contributor(s): Lucas, Simon M [author.] | Gaina, Raluca D [author.] | Togelius, Julian [author.] | Khalifa, Ahmed [author.] | Liu, Jialin [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Games and Computational Intelligence: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: XIV, 177 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031021220.Subject(s): Mathematics | Engineering | Computational intelligence | Popular Culture | Artificial intelligence | Mathematics | Technology and Engineering | Computational Intelligence | Popular Culture | Artificial IntelligenceAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 510 Online resources: Click here to access online
Contents:
Preface -- Acknowledgments -- Introduction -- VGDL and the GVGAI Framework -- Planning in GVGAI -- Frontiers of GVGAI Planning -- Learning in GVGAI -- Procedural Content Generation in GVGAI -- Automatic General Game Tuning -- GVGAI without VGDL -- GVGAI: What's Next? -- Bibliography -- Authors' Biographies.
In: Springer Nature eBookSummary: Research on general video game playing aims at designing agents or content generators that can perform well in multiple video games, possibly without knowing the game in advance and with little to no specific domain knowledge. The general video game AI framework and competition propose a challenge in which researchers can test their favorite AI methods with a potentially infinite number of games created using the Video Game Description Language. The open-source framework has been used since 2014 for running a challenge. Competitors around the globe submit their best approaches that aim to generalize well across games. Additionally, the framework has been used in AI modules by many higher-education institutions as assignments, or as proposed projects for final year (undergraduate and Master's) students and Ph.D. candidates. The present book, written by the developers and organizers of the framework, presents the most interesting highlights of the research performed by the authors duringthese years in this domain. It showcases work on methods to play the games, generators of content, and video game optimization. It also outlines potential further work in an area that offers multiple research directions for the future.
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Preface -- Acknowledgments -- Introduction -- VGDL and the GVGAI Framework -- Planning in GVGAI -- Frontiers of GVGAI Planning -- Learning in GVGAI -- Procedural Content Generation in GVGAI -- Automatic General Game Tuning -- GVGAI without VGDL -- GVGAI: What's Next? -- Bibliography -- Authors' Biographies.

Research on general video game playing aims at designing agents or content generators that can perform well in multiple video games, possibly without knowing the game in advance and with little to no specific domain knowledge. The general video game AI framework and competition propose a challenge in which researchers can test their favorite AI methods with a potentially infinite number of games created using the Video Game Description Language. The open-source framework has been used since 2014 for running a challenge. Competitors around the globe submit their best approaches that aim to generalize well across games. Additionally, the framework has been used in AI modules by many higher-education institutions as assignments, or as proposed projects for final year (undergraduate and Master's) students and Ph.D. candidates. The present book, written by the developers and organizers of the framework, presents the most interesting highlights of the research performed by the authors duringthese years in this domain. It showcases work on methods to play the games, generators of content, and video game optimization. It also outlines potential further work in an area that offers multiple research directions for the future.

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