國立虎尾科技大學 |

General video game artificial intelligence /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	General video game artificial intelligence // Diego P�erez Li�ebana, Simon M. Lucas, and Raluca D. Gaina, Julian Togelius and Ahmed Khalifa, Jialin Liu.
作者:	P�erez Li�ebana, Diego,
其他作者:	Lucas, Simon M.,
面頁冊數:	1 PDF (xiii, 177 pages) :illustrations (some color). :
附註:	Part of: Synthesis digital library of engineering and computer science.
標題:	Computer games. -
電子資源:	https://doi.org/10.2200/S00944ED1V01Y201908GCI005
電子資源:	https://ieeexplore.ieee.org/servlet/opac?bknumber=8866867
ISBN:	9781681736457

General video game artificial intelligence /
P�erez Li�ebana, Diego,

General video game artificial intelligence /Diego P�erez Li�ebana, Simon M. Lucas, and Raluca D. Gaina, Julian Togelius and Ahmed Khalifa, Jialin Liu. - 1 PDF (xiii, 177 pages) :illustrations (some color). - Synthesis lectures on games and computational intelligence,#52573-6493 ;. - Synthesis digital library of engineering and computer science..

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

Includes bibliographical references (pages 163-174).

1. Introduction / Diego P�erez Li�ebana -- 1.1. A historical view : from chess to GVGAI -- 1.2. GVGAI in education -- 1.3. GVGAI and the games industry

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

Compendex

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.

Mode of access: World Wide Web.

ISBN: 9781681736457

Standard No.: 10.2200/S00944ED1V01Y201908GCI005doiSubjects--Topical Terms:

562997
Computer games.
Subjects--Index Terms:

computational intelligence

LC Class. No.: GV1469.15 / .P474 2019eb

Dewey Class. No.: 794.8

General video game artificial intelligence /
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020 $z 9781681736464 $q hardcover
020 $z 9781681736440 $q paperback
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245 1 0 $a General video game artificial intelligence / $c Diego P�erez Li�ebana, Simon M. Lucas, and Raluca D. Gaina, Julian Togelius and Ahmed Khalifa, Jialin Liu.
264 1 $a [San Rafael, California] : $b Morgan & Claypool, $c [2019]
300 $a 1 PDF (xiii, 177 pages) : $b illustrations (some color).
336 $a text $2 rdacontent
337 $a electronic $2 isbdmedia
338 $a online resource $2 rdacarrier
490 1 $a Synthesis lectures on games and computational intelligence, $x 2573-6493 ; $v #5
500 $a Part of: Synthesis digital library of engineering and computer science.
504 $a Includes bibliographical references (pages 163-174).
505 0 $a 1. Introduction / Diego P�erez Li�ebana -- 1.1. A historical view : from chess to GVGAI -- 1.2. GVGAI in education -- 1.3. GVGAI and the games industry
505 8 $a 2. VGDL and the GVGAI framework / Diego P�erez Li�ebana -- 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
505 8 $a 3. Planning in GVGAI / Diego P�erez Li�ebana 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
505 8 $a 4. Frontiers of GVGAI planning / Diego P�erez Li�ebana 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
505 8 $a 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
505 8 $a 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
505 8 $a 7. Automatic general game tuning / Diego P�erez Li�ebana -- 7.1. Introduction -- 7.2. GVGAI parameterization -- 7.3. Evolving games for different agents -- 7.4. Modeling player experience -- 7.5. Exercises
505 8 $a 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 P�erez Li�ebana.
506 $a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 $a Compendex
510 0 $a INSPEC
510 0 $a Google scholar
510 0 $a Google book search
520 $a 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.
530 $a Also available in print.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Title from PDF title page (viewed on October 27, 2019).
650 0 $a Computer games. $3 562997
650 0 $a Artificial intelligence. $3 559380
650 0 $a Video games $x Design. $3 564223
653 $a computational intelligence
653 $a artificial intelligence
653 $a video games
653 $a general video game playing
653 $a GVGAI
653 $a video game description language
653 $a reinforcement learning
653 $a Monte Carlo tree search
653 $a rolling horizon evolutionary algorithms
653 $a procedural content generation
700 1 $a Lucas, Simon M., $e author. $3 1253139
700 1 $a Gaina, Raluca D., $e author. $3 1253140
700 1 $a Togelius, Julian, $e author. $3 1253141
700 1 $a Khalifa, Ahmed, $e author. $3 1253142
700 1 $a Liu, Jialin, $e author. $3 1253143
776 0 8 $i Print version: $z 9781681736464 $z 9781681736440
830 0 $a Synthesis digital library of engineering and computer science. $3 598254
830 0 $a Synthesis lectures on games and computational intelligence ; $v #2. $3 1166800
856 4 0 $3 Abstract with links to full text $u https://doi.org/10.2200/S00944ED1V01Y201908GCI005
856 4 2 $3 Abstract with links to resource $u https://ieeexplore.ieee.org/servlet/opac?bknumber=8866867