在已知和部分已知的游戏地图中快速捕获猎物的快速算法

Jorge A. Baier, A. Botea, Daniel Damir Harabor, Carlos Hernández
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引用次数: 11

摘要

在移动目标搜索中,目标是引导猎人捕捉移动的猎物。尽管在游戏应用程序中,地图在开发时总是可用的,但当前的移动目标搜索方法并没有利用预处理来提高搜索性能。在本文中,我们提出了MtsCopa算法,该算法利用压缩路径数据库(CPDs)形式的预计算信息,能够在已知和部分已知的地形中引导猎人代理。cpd先前已用于标准的固定目标寻路,但尚未用于移动目标搜索。我们在标准游戏地图上评估了MtsCopa。我们的速度结果比目前的技术水平好几个数量级。每个个体移动的时间得到了改善,这在实时搜索场景中很重要,在实时搜索场景中,移动的可用时间是有限的。与最先进的技术相比,猎人移动的数量通常更好,因为cpd提供了沿着最短路径的最佳移动。与以前成功的方法(如I-ARA*)相比,我们的方法易于理解和实现。此外,我们还证明了MtsCopa总是在可能的情况下引导agent捕捉猎物。
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Fast Algorithm for Catching a Prey Quickly in Known and Partially Known Game Maps
In moving target search, the objective is to guide a hunter agent to catch a moving prey. Even though in game applications maps are always available at developing time, current approaches to moving target search do not exploit preprocessing to improve search performance. In this paper, we propose MtsCopa, an algorithm that exploits precomputed information in the form of compressed path databases (CPDs), and that is able to guide a hunter agent in both known and partially known terrain. CPDs have previously been used in standard, fixed-target pathfinding but had not been used in the context of moving target search. We evaluated MtsCopa over standard game maps. Our speed results are orders of magnitude better than current state of the art. The time per individual move is improved, which is important in real-time search scenarios, where the time available to make a move is limited. Compared to state of the art, the number of hunter moves is often better and otherwise comparable, since CPDs provide optimal moves along shortest paths. Compared to previous successful methods, such as I-ARA*, our method is simple to understand and implement. In addition, we prove MtsCopa always guides the agent to catch the prey when possible.
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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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