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General video game artificial intelligence /

By: Pérez Liébana, Diego [author.].
Contributor(s): Lucas, Simon M [author.] | Gaina, Raluca D [author.] | Togelius, Julian [author.] | Khalifa, Ahmed [author.] | Liu, Jialin [author.].
Material type: materialTypeLabelBookSeries: Synthesis digital library of engineering and computer science: ; Synthesis lectures on games and computational intelligence: #5.Publisher: [San Rafael, California] : Morgan & Claypool, [2019]Description: 1 PDF (xiii, 177 pages) : illustrations (some color).Content type: text Media type: electronic Carrier type: online resourceISBN: 9781681736457.Subject(s): Computer games | Artificial intelligence | Video games -- Design | computational intelligence | artificial intelligence | video games | general video game playing | GVGAI | video game description language | reinforcement learning | Monte Carlo tree search | rolling horizon evolutionary algorithms | procedural content generationDDC classification: 794.8 Online resources: Abstract with links to full text | Abstract with links to resource Also available in print.
Contents:
1. Introduction / Diego Pérez Liébana -- 1.1. A historical view : from chess to GVGAI -- 1.2. GVGAI in education -- 1.3. GVGAI and the games industry
2. VGDL and the GVGAI framework / Diego Pérez Liébana -- 2.1. Introduction -- 2.2. The video game description language -- 2.3. The general video game AI framework -- 2.4. The GVGAI competition -- 2.5. Exercises
3. Planning in GVGAI / Diego Pérez Liébana and Raluca D. Gaina -- 3.1. Introduction -- 3.2. Monte Carlo tree search -- 3.3. Knowledge-based fast evolutionary MCTS -- 3.4. Multi-objective MCTS for GVGAI -- 3.5. Rolling horizon evolutionary algorithms -- 3.6. Exercises
4. Frontiers of GVGAI planning / Diego Pérez Liébana and Raluca D. Gaina -- 4.1. Introduction -- 4.2. State of the art in GVGAI planning -- 4.3. Current problems in GVGAI planning -- 4.4. General win prediction in GVGAI -- 4.5. Exercises
5. Learning in GVGAI / Jialin Liu -- 5.1. Challenges of learning in GVGAI -- 5.2. Framework -- 5.3. GVGAI learning competitions -- 5.4. Competition entries -- 5.5. Summary -- 5.6. Exercises
6. Procedural content generation in GVGAI / Ahmed Khalifa and Julian Togelius -- 6.1. Level generation in GVGAI -- 6.2. Rule generation in GVGAI -- 6.3. Exercises
7. Automatic general game tuning / Diego Pérez Liébana -- 7.1. Introduction -- 7.2. GVGAI parameterization -- 7.3. Evolving games for different agents -- 7.4. Modeling player experience -- 7.5. Exercises
8. GVGAI without VGDL / Simon M. Lucas -- 8.1. Introduction -- 8.2. Implementation principles -- 8.3. Interfacing -- 8.4. Sample Java games -- 8.5. Conclusions -- 8.6. Exercises -- 9. GVGAI : what's next? / Diego Pérez Liébana.
Summary: 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 during these 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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Item type Current location Call number Status Date due Barcode Item holds
E books E books PK Kelkar Library, IIT Kanpur
Available EBKE943
Total holds: 0

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 163-174).

1. Introduction / Diego Pérez Liébana -- 1.1. A historical view : from chess to GVGAI -- 1.2. GVGAI in education -- 1.3. GVGAI and the games industry

2. VGDL and the GVGAI framework / Diego Pérez Liébana -- 2.1. Introduction -- 2.2. The video game description language -- 2.3. The general video game AI framework -- 2.4. The GVGAI competition -- 2.5. Exercises

3. Planning in GVGAI / Diego Pérez Liébana and Raluca D. Gaina -- 3.1. Introduction -- 3.2. Monte Carlo tree search -- 3.3. Knowledge-based fast evolutionary MCTS -- 3.4. Multi-objective MCTS for GVGAI -- 3.5. Rolling horizon evolutionary algorithms -- 3.6. Exercises

4. Frontiers of GVGAI planning / Diego Pérez Liébana and Raluca D. Gaina -- 4.1. Introduction -- 4.2. State of the art in GVGAI planning -- 4.3. Current problems in GVGAI planning -- 4.4. General win prediction in GVGAI -- 4.5. Exercises

5. Learning in GVGAI / Jialin Liu -- 5.1. Challenges of learning in GVGAI -- 5.2. Framework -- 5.3. GVGAI learning competitions -- 5.4. Competition entries -- 5.5. Summary -- 5.6. Exercises

6. Procedural content generation in GVGAI / Ahmed Khalifa and Julian Togelius -- 6.1. Level generation in GVGAI -- 6.2. Rule generation in GVGAI -- 6.3. Exercises

7. Automatic general game tuning / Diego Pérez Liébana -- 7.1. Introduction -- 7.2. GVGAI parameterization -- 7.3. Evolving games for different agents -- 7.4. Modeling player experience -- 7.5. Exercises

8. GVGAI without VGDL / Simon M. Lucas -- 8.1. Introduction -- 8.2. Implementation principles -- 8.3. Interfacing -- 8.4. Sample Java games -- 8.5. Conclusions -- 8.6. Exercises -- 9. GVGAI : what's next? / Diego Pérez Liébana.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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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 during these 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.

Also available in print.

Title from PDF title page (viewed on October 27, 2019).

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