计算池的分布式代理

Q2 Computer Science

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2016-04-01 DOI:10.1109/TCIAIG.2016.2549748

Christopher Archibald, Alon Altman, Y. Shoham

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引用次数: 3

摘要

从人工智能(AI)的角度来看，具有连续状态和动作空间的游戏呈现出独特的挑战。台球，或台球，就是这样一个领域，已经成为几个研究的焦点，旨在设计人工智能代理成功地玩。由于行为的连续性，我们很自然地认为智能体调查行为的时间越长，它的表现就越好。本文给出了一个成功的智能体的详细描述，该智能体具有新颖的分布式架构，旨在通过使用多个cpu来为射击模拟和分析提供更多的时间。简要分析了代理的分布式组件，以及由此获得的额外时间对其成功的贡献，特别是与其他新组件相比。所描述的代理CueCard赢得了2008年举行的计算机奥林匹克计算池锦标赛。

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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A Distributed Agent for Computational Pool

Games with continuous state and action spaces present unique challenges from an artificial intelligence (AI) viewpoint. Billiards, or pool, is one such domain that has been the focus of several research efforts aimed at designing AI agents to play successfully. Due to the continuous nature of the actions, it is natural to believe that the more time an agent has to investigate actions, the better it will perform. This paper gives a thorough description of a successful agent with a novel distributed architecture, designed for being able to grant further time for shot simulation and analysis through the utilization of many CPUs. A brief analysis of the distributed component of the agent is presented, as well as how much the extra time thus obtained contributed to its success, especially when compared to its other novel components. The described agent, CueCard, won the Computer Olympiad computational pool tournament held in 2008.

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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.