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Reusing cost-minimal paths for goal-directed navigation in partially known terrains

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Abstract

Situated agents frequently need to solve search problems in partially known terrains in which the costs of the arcs of the search graphs can increase (but not decrease) when the agents observe new information. An example of such search problems is goal-directed navigation with the freespace assumption in partially known terrains, where agents repeatedly follow cost-minimal paths from their current locations to given goal locations. Incremental heuristic search is an approach for solving the resulting sequences of similar search problems potentially faster than with classical heuristic search, by reusing information from previous searches to speed up its current search. There are two classes of incremental heuristic search algorithms, namely those that make the \(h\)-values of the current search more informed (such as Adaptive A*) and those that reuse parts of the A* search trees of previous searches during the current search (such as D* Lite). In this article, we introduce Path-Adaptive A* and its generalization Tree-Adaptive A*. Both incremental heuristic search algorithms terminate their searches before they expand the goal state, namely when they expand a state that is on a provably cost-minimal path to the goal. Path-Adaptive A* stores a single cost-minimal path to the goal state (the reusable path), while Tree-Adaptive A* stores a set of cost-minimal paths to the goal state (the reusable tree), and is thus potentially more efficient than Path-Adaptive A* since it uses information from all previous searches and not just the last one. Tree-Adaptive A* is the first incremental heuristic search algorithm that combines the principles of both classes of incremental heuristic search algorithms. We demonstrate experimentally that both Path-Adaptive A* and Tree-Adaptive A* can be faster than Adaptive A* and D* Lite, two state-of-the-art incremental heuristic search algorithms for goal-directed navigation with the freespace assumption.

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Notes

  1. All game and office maps can be found in Nathan Sturtevant’s repository [24].

  2. All game maps can be found in Nathan Sturtevant’s repository at http://www.movingai.com/benchmarks/.

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Acknowledgments

This material is based upon work supported by NSF (while Sven Koenig was serving at NSF). It is also based upon work supported by Fondecyt-Chile under contract/Grant number 11080063, NSF under contract/Grant number 115-1319966, ARL/ARO under contract/Grant number W911NF-08-1-0468, ONR in form of a MURI under contract/Grant number N00014-09-1-1031, DOT under contract/Grant number DTFH61-11-C-00010 and the Spanish Ministry of Science and Innovation under Grant number TIN2009-13591-C02-02. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations, agencies, companies or the U.S. government. We thank Nathan Sturtevant for making his maps available at movingai.com.

Author information

Authors and Affiliations

  1. Departamento de Ingeniería Informática, Universidad Católica de la Ssma. Concepción, Concepción, Chile

    Carlos Hernández

  2. Computer Science Department, University of Southern California, Los Angeles, CA , 90089-0781, USA

    Tansel Uras & Sven Koenig

  3. Departamento de Ciencia de la Computación, Pontificia Universidad Católica de Chile, Santiago, Chile

    Jorge A. Baier

  4. Google, 1600 Amphitheatre Parkway, Mountain View, CA , 94043, USA

    Xiaoxun Sun

  5. IIIA - CSIC, Campus Universitat Autonòma de Barcelona, 08193 , Bellaterra, Spain

    Pedro Meseguer

Authors
  1. Carlos Hernández
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  2. Tansel Uras
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  3. Sven Koenig
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  4. Jorge A. Baier
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  5. Xiaoxun Sun
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  6. Pedro Meseguer
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Corresponding author

Correspondence to Carlos Hernández.

Appendix

Appendix

See Tables 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10.

Table 1 4-Neighbor game maps
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Table 2 8-Neighbor game maps
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Table 3 4-Neighbor office maps
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Table 4 8-Neighbor office maps
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Table 5 4-Neighbor maps with randomly blocked cells
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Table 6 8-Neighbor maps with randomly blocked cells
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Table 7 4-Neighbor populated game maps
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Table 8 8-Neighbor populated game maps
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Table 9 4-Neighbor populated office maps
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Table 10 8-Neighbor populated office maps
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Hernández, C., Uras, T., Koenig, S. et al. Reusing cost-minimal paths for goal-directed navigation in partially known terrains. Auton Agent Multi-Agent Syst 29, 850–895 (2015). https://doi.org/10.1007/s10458-014-9266-0

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