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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Diego Perez Liebana is a Lecturer in Computer Games and AI at Queen Mary University of London (QMUL) and hold a Ph.D. in Computer Science from the University of Essex (2015). His research interests are search algorithms, evolutionary computation, and reinforcement learning applied to real-time games and general video game playing. He is published more than 60 papers in leading conferences and journals in the area, including best paper awards (CIG, EvoStar). He is the main organizer behind popular AI game-based competitions in the field, serves as a reviewer in top conferences and journals, and he was general chair of the 2019 IEEE Conference on Games (QMUL). He has experience in the video games industry with titles published for both PC and consoles, and also in developing AI tools for games.
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