Predicting Opponent's Production in Real-Time Strategy Games With Answer Set Programming

Marius Stanescu, Michal Čertický
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引用次数: 21

Abstract

The adversarial character of real-time strategy (RTS) games is one of the main sources of uncertainty within this domain. Since players lack exact knowledge about their opponent's actions, they need a reasonable representation of alternative possibilities and their likelihood. In this article we propose a method of predicting the most probable combination of units produced by the opponent during a certain time period. We employ a logic programming paradigm called Answer Set Programming, since its semantics is well suited for reasoning with uncertainty and incomplete knowledge. In contrast with typical, purely probabilistic approaches, the presented method takes into account the background knowledge about the game and only considers the combinations that are consistent with the game mechanics and with the player's partial observations. Experiments, conducted during different phases of StarCraft: Brood War and Warcraft III: The Frozen Throne games, show that the prediction accuracy for time intervals of 1-3 min seems to be surprisingly high, making the method useful in practice. Root-mean-square error grows only slowly with increasing prediction intervals-almost in a linear fashion.
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基于答案集编程的实时策略游戏对手产出预测
即时战略(RTS)游戏的对抗特征是这一领域不确定性的主要来源之一。因为玩家对对手的行动缺乏确切的了解,所以他们需要一个合理的替代可能性及其可能性的表示。在这篇文章中,我们提出了一种方法来预测对手在一定时间内最可能产生的单位组合。我们采用了一种称为答案集编程的逻辑编程范式,因为它的语义非常适合不确定性和不完整知识的推理。与典型的纯概率方法相比,本文提出的方法考虑了游戏的背景知识,只考虑与游戏机制和玩家的部分观察相一致的组合。在《星际争霸:母巢之战》和《魔兽争霸III:冰封王座》游戏的不同阶段进行的实验表明,在1-3分钟的时间间隔内,该方法的预测精度似乎高得惊人,这使得该方法在实践中很有用。均方根误差随着预测间隔的增加而缓慢增长,几乎呈线性增长。
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来源期刊
IEEE Transactions on Computational Intelligence and AI in Games
IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
4.60
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.
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