Lab Papers (GRASP)

Document Type

Journal Article

Date of this Version

9-2008

Comments

Copyright 2008 Elsevier. Reprinted from:
Maxim Likhachev, Dave Ferguson, Geoff Gordon, Anthony Stentz, Sebastian Thrun, Anytime search in dynamic graphs, Artificial Intelligence, Volume 172, Issue 14, September 2008, Pages 1613-1643, ISSN 0004-3702.
DOI: 10.1016/j.artint.2007.11.009.
URL: http://www.sciencedirect.com/science/article/B6TYF-4SFS0P8-1/2/281d52a575110d9711291fbaa9210d11

Abstract

Agents operating in the real world often have limited time available for planning their next actions. Producing optimal plans is infeasible in these scenarios. Instead, agents must be satisfied with the best plans they can generate within the time available. One class of planners well-suited to this task are anytime planners, which quickly find an initial, highly suboptimal plan, and then improve this plan until time runs out. A second challenge associated with planning in the real world is that models are usually imperfect and environments are often dynamic. Thus, agents need to update their models and consequently plans over time. Incremental planners, which make use of the results of previous planning efforts to generate a new plan, can substantially speed up each planning episode in such cases. In this paper, we present an A^*-based anytime search algorithm that produces significantly better solutions than current approaches, while also providing suboptimality bounds on the quality of the solution at any point in time. We also present an extension of this algorithm that is both anytime and incremental. This extension improves its current solution while deliberation time allows and is able to incrementally repair its solution when changes to the world model occur. We provide a number of theoretical and experimental results and demonstrate the effectiveness of the approaches in a robot navigation domain involving two physical systems. We believe that the simplicity, theoretical properties, and generality of the presented methods make them well suited to a range of search problems involving dynamic graphs.

Keywords

A?, Anytime planning, Anytime search, Heuristic search, Incremental search, Planning, Replanning, Search

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Date Posted: 29 September 2009

This document has been peer reviewed.