Abstract
In general, games pose interesting and complex problems for the implementation of intelligent agents and are a popular domain in the study of artificial intelligence. In fact, games have been at the center of some of the most well-known achievements in artificial intelligence. From classical board games such as chess, checkers, backgammon and Go, to video games such as Dota 2 and StarCraft II, artificial intelligence research has devised computer programs that can play at the level of a human master and even at a human world champion level. Planning and learning, two well-known and successful paradigms of artificial intelligence, have greatly contributed to these achievements. Although representing distinct approaches, planning and learning try to solve similar problems and share some similarities. They can even complement each other. This has led to research on methodologies to combine the strengths of both approaches to derive better solutions. This paper presents a survey of the multiple methodologies that have been proposed to integrate planning and learning in the context of games. In order to provide a richer contextualization, the paper also presents learning and planning techniques commonly used in games, both in terms of their theoretical foundations and applications.
Highlights
Games have always been widely used as a development and testing environment in the study of artificial intelligence (AI) in academia [1]
The fact that games are well defined by explicit rules, vary greatly in the challenges they pose (e.g., the challenges posed by a puzzle game are very different from those posed by a real time strategy (RTS) game) and that the techniques developed in the domain of games can be transferred to other research fields are some of the aspects that have greatly contributed to this [1]
While initial research on game AI focused on classical board games such as chess and checkers, more recently video games have attracted significant research interest, a fact proven by the several international competitions (e.g., the General Video Game AI (GVGAI) competition [2], the General Game Playing (GGP) competition [3], and the Geometry Friends Game AI competition [4]) held annually in multiple international conferences dedicated to game AI (e.g., the Conference on Games (CoG), formerly Conference on Computational Intelligence and Games (CIG), the Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), and the International Conference on the Foundations of Digital Games (FDG))
Summary
Games have always been widely used as a development and testing environment in the study of artificial intelligence (AI) in academia [1]. While initial research on game AI focused on classical board games such as chess and checkers, more recently video games have attracted significant research interest, a fact proven by the several international competitions (e.g., the General Video Game AI (GVGAI) competition [2], the General Game Playing (GGP) competition [3], and the Geometry Friends Game AI competition [4]) held annually in multiple international conferences dedicated to game AI (e.g., the Conference on Games (CoG), formerly Conference on Computational Intelligence and Games (CIG), the Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), and the International Conference on the Foundations of Digital Games (FDG)) This growing interest has been fueled by an increasing commercial interest by the gaming industry on finding solutions to create games that are more enjoyable, challenging and engaging, and that provide a more personalized experience to the human players, in the form of, for example, more sophisticated.
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