RTS游戏的多尺度贝叶斯建模:在星际争霸AI中的应用

Q2 Computer Science

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2016-12-01 DOI:10.1109/TCIAIG.2015.2487743

Gabriel Synnaeve, P. Bessière

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引用次数: 31

摘要

本文展示了贝叶斯模型在即时战略(RTS)游戏AI中的三个不同核心组件的使用:微观管理(单位控制)，战术(军队移动和位置)和战略(经济，技术，生产，军队类型)。拥有端到端概率模型的优势在于，特定变量上的分布可用于连接不同抽象层次上的不同模型。我们将此模型应用于《星际争霸》，并独立评估每个模型。在此过程中，我们制作并发布了RTS机器学习的综合数据集。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Multiscale Bayesian Modeling for RTS Games: An Application to StarCraft AI

This paper showcases the use of Bayesian models for real-time strategy (RTS) games AI in three distinct core components: micromanagement (units control), tactics (army moves and positions), and strategy (economy, technology, production, army types). The strength of having end-to-end probabilistic models is that distributions on specific variables can be used to interconnect different models at different levels of abstraction. We applied this modeling to StarCraft, and evaluated each model independently. Along the way, we produced and released a comprehensive data set for RTS machine learning.

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来源期刊

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

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.