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Propagating Updates in Real-Time Search: HLRTA*(k)

  • Carlos Hernández
  • Pedro Meseguer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4177)

Abstract

We enhance real-time search algorithms with bounded propagation of heuristic changes. When the heuristic of the current state is updated, this change is propagated consistently up to k states. Applying this idea to HLRTA*, we have developed the new HLRTA*(k) algorithm, which shows a clear performance improvement over HLRTA*. Experimentally, HLRTA*(k) converges in less trials than LRTA*(k), while the contrary was true for these algorithms without propagation. We provide empirical results showing the benefits of our approach.

Keywords

Optimal Path Goal State Bounded Propagation Action Execution Heuristic Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Carlos Hernández
    • 1
  • Pedro Meseguer
    • 1
  1. 1.Institut d’Investigació en Intel.ligència ArtificialConsejo Superior de Investigaciones Científicas, Campus UABBellaterraSpain

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