自适应并行迭代深化搜索
Adaptive Parallel Iterative Deepening Search
摘要：
迄今为止开发的许多人工智能技术依赖于通过大空间的启发式搜索。不幸的是，这些空间的大小和相应的计算工作量降低了其他新颖和有效的算法的适用性。许多并行和分布式搜索方法大大提高了搜索过程的性能。我们的目标是开发一个架构，自动选择并行搜索策略的各种搜索问题的最佳性能。在本文中，我们描述了这样一个体系结构实现的尤里卡系统，它结合了许多不同的方法来并行启发式搜索的好处。通过实证和理论分析，我们观察到问题空间的特征直接影响最优并行搜索策略的选择。然后，我们采用机器学习技术来选择一个给定的问题空间的最佳并行搜索策略。当一个新的搜索任务输入到系统中时，Eureka使用描述搜索空间和所选架构的特征来自动选择适当的搜索策略。Eureka已经在MIMD并行处理器、分布式工作站网络和使用多线程的单个工作站上进行了测试。从15个难题问题，机器人手臂运动问题，人工搜索空间，规划问题产生的结果表明，尤里卡优于任何测试策略专门用于所有问题的实例，并能够大大减少搜索时间，这些应用程序。
英文摘要：
Many of the artificial intelligence techniques developed to date rely on heuristic search through large spaces. Unfortunately, the size of these spaces and the corresponding computational effort reduce the applicability of otherwise novel and effective algorithms. A number of parallel and distributed approaches to search have considerably improved the performance of the search process. Our goal is to develop an architecture that automatically selects parallel search strategies for optimal performance on a variety of search problems. In this paper we describe one such architecture realized in the Eureka system, which combines the benefits of many different approaches to parallel heuristic search. Through empirical and theoretical analyses we observe that features of the problem space directly affect the choice of optimal parallel search strategy. We then employ machine learning techniques to select the optimal parallel search strategy for a given problem space. When a new search task is input to the system, Eureka uses features describing the search space and the chosen architecture to automatically select the appropriate search strategy. Eureka has been tested on a MIMD parallel processor, a distributed network of workstations, and a single workstation using multithreading. Results generated from fifteen puzzle problems, robot arm motion problems, artificial search spaces, and planning problems indicate that Eureka outperforms any of the tested strategies used exclusively for all problem instances and is able to greatly reduce the search time for these applications.
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。
