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Permanent link (DOI): https://doi.org/10.7939/R3XW47Z9H

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Better Time Constrained Search via Randomization and Postprocessing Open Access

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Author or creator
Xie, Fan
Valenzano, Richard
Müller, Martin
Additional contributors
Subject/Keyword
planning
postprocessing
Artificial Intelligence
Type of item
Computing Science Technical Report
Computing science technical report ID
TR13-02
Language
English
Place
Time
Description
Most of the satisficing planners which are based on heuristic search iteratively improve their solution quality through an anytime approach. Typically, the lowest-cost solution found so far is used to constrain the search. This avoids areas of the state space which cannot directly lead to lower cost solutions. However, in conjunction with a post-processing plan improvement system such as ARAS, this bounding approach can harm a planner’s performance. The new anytime search framework of Diverse Any-Time Search avoids this behaviour by taking advantage of the fact that post-processing can often improve a lower quality input plan to a superior final plan. The framework encourages diversity of “raw” plans by restarting and using randomization, and does not use previous solutions for bounding. This gives a post-processing system a more diverse set of plans to work on, which improves performance. When adding both Diverse Any-Time Search and the ARAS post-processor to LAMA- 2011, the winner of the most recent IPC planning competi- tion, and AEES, the Anytime Explicit Estimation Algorithm, the performance on the 550 IPC 2008 and IPC 2011 problems is improved by almost 60 points according to the IPC metric, from 511 to over 570 on LAMA-2011, and 73 points from 440 to over 513 on AEES.
Date created
2013
DOI
doi:10.7939/R3XW47Z9H
License information
Creative Commons Attribution 3.0 Unported
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File title: Better Time Constrained Search via Randomization and Postprocessing
File author: Fan Xie, Richard Valenzano, Martin Mller
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