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The Joy of Forgetting: Faster Anytime Search via Restarting

机译:忘记的喜悦:通过重新启动可以更快地随时搜索

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摘要

Anytime search algorithms solve optimisation problems by quickly finding a (usually suboptimal) first solution and then finding improved solutions when given additional time. To deliver an initial solution quickly, they are typically greedy with respect to the heuristic cost-to-go estimate h. In this paper, we show that this low-h bias can cause poor performance if the greedy search makes early mistakes. Building on this observation, we present a new anytime approach that restarts the search from the initial state every time a new solution is found. We demonstrate the utility of our method via experiments in PDDL planning as well as other domains, and show that it is particularly useful for problems where the heuristic has systematic errors.
机译:任何时候搜索算法都可以通过快速找到(通常是次优的)第一个解决方案,然后在给定额外时间的情况下找到改进的解决方案来解决优化问题。为了快速提供初始解决方案,他们通常对启发式成本估算h感到贪婪。在本文中,我们表明,如果贪婪搜索犯了早期错误,则这种低h偏差会导致性能不佳。基于这种观察,我们提出了一种随时随地的新方法,每当找到新的解决方案时,该方法就会从初始状态重新开始搜索。我们通过PDDL计划以及其他领域的实验证明了我们方法的实用性,并表明该方法对于启发式系统错误的问题特别有用。

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