基于统计树的滚动时域进化算法的种群播种
Statistical Tree-based Population Seeding for Rolling Horizon EAs in General Video Game Playing
摘要：
近年来，已经提出了多种人工智能（AI）方法来创建控制器来玩不同性质和复杂性的多个视频游戏，而不会向AI方法揭示这些游戏中的每一个的特定机制。近年来，采用滚动时域机制的进化算法在这类问题中取得了令人瞩目的成果。然而，滚动地平线EA存在一些局限性，使其成为AI的一个巨大挑战。这些限制包括创建一个种群并在几分之一秒内进化它以提出游戏代理执行的动作的浪费机制。另一个限制是使用标量值（适应度值）来指导进化搜索，而不是考虑一种机制，该机制告知我们特定代理在滚动时域模拟期间的行为。在这项工作中，我们解决了这两个问题。我们介绍了使用的统计树，解决后者的限制。此外，我们通过采用一种机制来解决前一个限制，该机制允许我们使用蒙特卡罗树搜索来播种部分人口，这种方法在多个通用视频游戏AI竞赛中占据主导地位。我们展示了所提出的新机制，称为基于统计树的人口播种，实现更好的结果相比，香草滚动地平线EA在一组20场比赛，包括10个随机和10个确定性的游戏。
英文摘要：
Multiple Artificial Intelligence (AI) methods have been proposed over recent years to create controllers to play multiple video games of different nature and complexity without revealing the specific mechanics of each of these games to the AI methods. In recent years, Evolutionary Algorithms (EAs) employing rolling horizon mechanisms have achieved extraordinary results in these type of problems. However, some limitations are present in Rolling Horizon EAs making it a grand challenge of AI. These limitations include the wasteful mechanism of creating a population and evolving it over a fraction of a second to propose an action to be executed by the game agent. Another limitation is to use a scalar value (fitness value) to direct evolutionary search instead of accounting for a mechanism that informs us how a particular agent behaves during the rolling horizon simulation. In this work, we address both of these issues. We introduce the use of a statistical tree that tackles the latter limitation. Furthermore, we tackle the former limitation by employing a mechanism that allows us to seed part of the population using Monte Carlo Tree Search, a method that has dominated multiple General Video Game AI competitions. We show how the proposed novel mechanism, called Statistical Tree-based Population Seeding, achieves better results compared to vanilla Rolling Horizon EAs in a set of 20 games, including 10 stochastic and 10 deterministic games.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
