article

Anytime heuristic search

Authors:

Eric A. Hansen,

Rong ZhouAuthors Info & Claims

Journal of Artificial Intelligence Research, Volume 28, Issue 1

Pages 267 - 297

Published: 01 March 2007 Publication History

Abstract

We describe how to convert the heuristic search algorithm A* into an anytime algorithm that finds a sequence of improved solutions and eventually converges to an optimal solution. The approach we adopt uses weighted heuristic search to find an approximate solution quickly, and then continues the weighted search to find improved solutions as well as to improve a bound on the suboptimality of the current solution. When the time available to solve a search problem is limited or uncertain, this creates an anytime heuristic search algorithm that allows a flexible tradeoff between search time and solution quality. We analyze the properties of the resulting Anytime A* algorithm, and consider its performance in three domains; sliding-tile puzzles, STRIPS planning, and multiple sequence alignment. To illustrate the generality of this approach, we also describe how to transform the memory-efficient search algorithm Recursive Best-First Search (RBFS) into an anytime algorithm.

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Published In

cover image Journal of Artificial Intelligence Research

Journal of Artificial Intelligence Research Volume 28, Issue 1

January 2007

550 pages

ISSN:1076-9757

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AI Access Foundation

El Segundo, CA, United States

Publication History

Published: 01 March 2007

Received: 01 May 2006

Published in JAIR Volume 28, Issue 1

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Cited By

Thelasingha NJulius AKim M(2022)Ensemble control of spatial variance of microbot systems through sequencing of motion primitives from optimal control trajectoriesIntelligent Service Robotics10.1007/s11370-022-00421-x15:2(215-230)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11370-022-00421-x
De Brugière TBaboulin MValiron BMartiel SAllouche C(2021)Gaussian Elimination versus Greedy Methods for the Synthesis of Linear Reversible CircuitsACM Transactions on Quantum Computing10.1145/34742262:3(1-26)Online publication date: 30-Sep-2021
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Lorberbom GMaddison CHeess NHazan TTarlow DLarochelle HRanzato MHadsell RBalcan MLin H(2020)Direct policy gradientsProceedings of the 34th International Conference on Neural Information Processing Systems10.5555/3495724.3497241(18076-18086)Online publication date: 6-Dec-2020
https://dl.acm.org/doi/10.5555/3495724.3497241
Bhattacharya S(2020)Towards optimal path computation in a simplicial complexInternational Journal of Robotics Research10.1177/027836491985542238:8(981-1009)Online publication date: 17-Jun-2020
https://dl.acm.org/doi/10.1177/0278364919855422
Bono MGerevini AHarabor DStuckey P(2019)Path planning with CPD heuristicsProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367032.3367203(1199-1205)Online publication date: 10-Aug-2019
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Tabakhi AYeoh WYokoo MElkind EVeloso MAgmon NTaylor M(2019)Parameterized Heuristics for Incomplete Weighted CSPs with Elicitation CostsProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331729(476-484)Online publication date: 8-May-2019
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Corrêa APereira ARitt M(2018)Analyzing tie-breaking strategies for the A* algorithmProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304652.3304664(4715-4721)Online publication date: 13-Jul-2018
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Da Silva Mendes RDe Santiago RLamb L(2018)Novel Parallel Anytime A* for Graph and Network Clustering2018 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2018.8477840(1-6)Online publication date: 8-Jul-2018
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