General video game artificial intelligence:
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
[San Rafael, California]
Morgan & Claypool Publishers
[2020]
|
| Schriftenreihe: | Synthesis lectures on games and computational intelligence
5 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xiii, 177 Seiten Illustrationen, Diagramme |
| ISBN: | 9781681736440 9781681736464 |
Internformat
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| 245 | 1 | 0 | |a General video game artificial intelligence |c Diego Pérez Liébana, Simon M. Lucas, and Raluca D. Gaina (Queen Mary University of London), Julian Togelius and Ahmed Khalifa (New York University), Jialin Liu (Southern University of Science and Technology) |
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Datensatz im Suchindex
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| adam_text | Vil Contents Preface......................................................................................... xi Acknowledgments ........................................................................................................xiii 1 Introduction...................................................................................................................... 1 Diego Pérez Liébana 1.1 1.2 1.3 2 A Historical View: From Chess to GVGAI.......................................................... 3 GVGAI in Education ............................................................................................. 7 GVGAI and the Games Industry...........................................................................9 j VGDL and the GVGAI Framework........................................................................... 13 Diego Pérez Liébana 2.1 2.2 2.3 2.4 2.5 3 Introduction............................................................................................................13 Tlie Video Game Description Language............................................................ 14 2.2.1 Continuous Plavsics................................................................................... 19 2.2.2 2-Player Games......................................................................................... 20 2.2.3 The Future of VGD L...............................................................................22 The General Video Game Al Framework............................................................ 22 2.3.1 API for 2-Player
Games...........................................................................24 2.3.2 GVGAI as a Multi-Track Challenge......................................................25 Tlie GVGAI Competition..................................................................................... 26 Exercises.................................................................................................................. 28 Planning in GVGAI....................................................................................................... 31 Diego Pérez Liébana and Rakıca D. Gaina 3.1 Introduction............................................................................................................31 3.2 3.3 Monte Carlo Tree Search....................................................................................... 32 Knowledge-Based Fast Evolutionary MCTS......................................................36 3.3.1 Fast Evolution in AICTS.........................................................................36 3.3.2 Learning Domain Knowledge................................................................ 37 3.3.3 Experimental Work................................................................................... 39
3.4 Multi-Objective MCTS tor GVGAI .............................................. 3.4.1 Multi-Objective Optimization............................................ 3.4.2 Multi-Objective MOTS....................................................... 3.4.3 Experimental Work............................................................... 40 41 42 44 3.5 Rolling Horizon Evolutionäre Algorithms...................................... 3.5.1 Vanilla RHEA....................................................................... 3.5.2 RHEA Enhancements......................................................... 49 49 52 3.6 Exercises.............................................................................................. 58 Frontiers of GVGAI Planning ............................................................ .61 Diezo О Pérez Liébana and Rabica D. Gaina 4.1 Introduction....................................................................................... (Վ 4.2 State ol the Art in GVGAI Planning.............................................. 4.2.1 О LETS (adrienctx)............................................................ 4.2.2 ToVo2 ................................................................................. 4.2.3 YOLOBOT ...................................................................... 62 Current Problems in GVGAI Planning........................................ General Win Prediction in GVGAI.............................................. 4.4.1 Game Playing Agents and Features................................. 4.4.2 Predictive
Models.............................................................. Exercises........................................................................................... бо Learning in GVGAI........................................................................... 79 4.3 4.4 4.5 62 ь2 64 6/ 67 Jialin Lin 5.1 Challenges of Learning in GVGAI............................................... 79 5.2 Framework...................................................................................... 5.2.1 GVGAI Learning Framework......................................... 5.2.2 GVGAI Learning Environment ..................................... 5.2.3 GVGAI Gym Environment............................................. 5.2.4 Comparing to Other Learning Frameworks................... GVGAI Learning Competitions................................................... 5.3.1 Competition Using the GVGAI Learning Environment 5.3.2 Competition Using the GVGAI Gym............................. Competition Entries ...................................................................... 5.4.1 Random Agent (Sample Agents)..................................... 5.4.2 DRL Algorithms (Sample Agents) ................................. 80 81 81 83 84 5.3 5.4 85 85 88 90 91 91
їх 6 5.4.3 Multi-Armed Bandit Algorithm............................................................91 5.4.4 Sarsa ...................................................................................................... 91 5.4.5 5.4.6 CLLeaming...............................................................................................92 Tree Search Methods.............................................................................. 92 5.4.7 Other Learning Agents ........................................................................92 5.4.8 Discussion................................................................................................ 93 5.5 Summary ...............................................................................................................94 5.6 Exercises.................................................................................................................94 Procedural Content Generation in GVGAI........................................................97 Ahmed Khalifa andJulian Togelius 6.1 6.2 6.3 7 Level Generation in GVGAI................................................................................ 98 6.1.1 6.1.2 Sample Generators................................................................................ 100 Competition and Other Generators......................................................104 6.1.3 Discussion.............................................................................................. 107 Rule Generation in GVGAI.............................................................................. 109 6.2.1
Sample Generators................................................................................ Ill 6.2.2 Other Generators.................................................................................. 115 6.2.3 Discussion...............................................................................................115 Exercises............................................................................................................... 115 Automatic General Game Tuning .....................................................................117 Diego Pérez Liébana 7.1 Introduction.........................................................................................................11՜ 7.1.1 Previous Work........................................................................................ 118 7.2 GVGAI Parameterization.................................................................................. 118 7.3 Evolving Games for Different Agents................................................................ 120 7.3.1 7.3.2 7.4 7.5 The /V -Tuple Bandit Evolutionary Algorithm..................................... 121 Variants of Aliens for Agents withDifferent Look-Aheads................125 Modeling Player Experience.............................................................................. 130 7.4.1 Designing the Search Space.................................................................. 131 7.4.2 Evolving Games for Player Experience................................................136
Exercises............................................................................................................... 143
8 GVGAI without VGDL..................................................... 145 Simon M. Lucas 8.1 8.2 8.3 8.4 8.5 8.6 9 145 Introduction.................................................................. Implementation Principles........................................... 8.2.1 Copying the Game State ............................... 8.2.2 Advancing the Game State............................. 14(i 14o Interfacing .................................................................... 8.3.1 Running GVGAI Agents on the Java Games 8.3.2 Running New Agents on the YGDL Games 144» Sample Java Games..................................................... 8.4.1 Asteroids......................................................... 8.4.2 Fast Planet Wars........................................... 8.4.3 Cave Swing................................................... 8.4.4 Speed............................................................. Conclusions ............................................................... Exercises...................................................................... 148 148 150 1.50 150 152 152 154 1.54 155 GVGAI: What’s Next?.......................................................................................... 159 Diego Pérez Liébana Bibliography...........................................................................................................163 Authors’ Biographies............................................................................................ 175
Series Editor: Daniel Ashlock, University of Guelph General Video Game Artificial Intelligence Diego Pérez Liébana, Queen Mary University ofLondon Simon M. Lucas, Queen Mary University ofLondon Rabica D. Gaina, Queen Mary University ofLondon Julian Togelius, New York University Ahmed Khalifa, New York University Jialin Liu, Southern University ofScience and Technology 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 fiirther work in an area that offers multiple research directions for the
fixture. ABOUT SYNTHESIS This volume is a printed version of a work that appears in the Synthesis Digital Library of Engineering and Computer Science. Synthesis lectures provide concise original presentations of important research and development topics, published quickly in digital and print formats. For more information, visit our website: http://store.morganclaypool.com
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| spelling | Pérez Liébana, Diego 1983- Verfasser (DE-588)1210182939 aut General video game artificial intelligence Diego Pérez Liébana, Simon M. Lucas, and Raluca D. Gaina (Queen Mary University of London), Julian Togelius and Ahmed Khalifa (New York University), Jialin Liu (Southern University of Science and Technology) [San Rafael, California] Morgan & Claypool Publishers [2020] xiii, 177 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Synthesis lectures on games and computational intelligence 5 Computerspiel (DE-588)4010457-6 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Computerspiel (DE-588)4010457-6 s Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Lucas, Simon M. ca. 20./21. Jh. Verfasser (DE-588)1225774454 aut Gaina, Raluca D. ca. 20./21. Jd. Verfasser (DE-588)122577456X aut Togelius, Julian Verfasser (DE-588)1166695727 aut Khalifa, Ahmed Verfasser (DE-588)1225774713 aut Liu, Jialin ca. 20./21. Jd. Verfasser (DE-588)1225774845 aut Erscheint auch als Online-Ausgabe 978-1-68173--645-7 Synthesis lectures on games and computational intelligence 5 (DE-604)BV045212326 5 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031628101&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031628101&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Pérez Liébana, Diego 1983- Lucas, Simon M. ca. 20./21. Jh Gaina, Raluca D. ca. 20./21. Jd Togelius, Julian Khalifa, Ahmed Liu, Jialin ca. 20./21. Jd General video game artificial intelligence Synthesis lectures on games and computational intelligence Computerspiel (DE-588)4010457-6 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4010457-6 (DE-588)4033447-8 |
| title | General video game artificial intelligence |
| title_auth | General video game artificial intelligence |
| title_exact_search | General video game artificial intelligence |
| title_full | General video game artificial intelligence Diego Pérez Liébana, Simon M. Lucas, and Raluca D. Gaina (Queen Mary University of London), Julian Togelius and Ahmed Khalifa (New York University), Jialin Liu (Southern University of Science and Technology) |
| title_fullStr | General video game artificial intelligence Diego Pérez Liébana, Simon M. Lucas, and Raluca D. Gaina (Queen Mary University of London), Julian Togelius and Ahmed Khalifa (New York University), Jialin Liu (Southern University of Science and Technology) |
| title_full_unstemmed | General video game artificial intelligence Diego Pérez Liébana, Simon M. Lucas, and Raluca D. Gaina (Queen Mary University of London), Julian Togelius and Ahmed Khalifa (New York University), Jialin Liu (Southern University of Science and Technology) |
| title_short | General video game artificial intelligence |
| title_sort | general video game artificial intelligence |
| topic | Computerspiel (DE-588)4010457-6 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Computerspiel Künstliche Intelligenz |
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| volume_link | (DE-604)BV045212326 |
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