Propagating updates in real-time search: HLRTA (k)

Authors:
Carlos Hernández;Pedro Meseguer
Affiliations:
Institut d'Investigació en Intel.ligència Artificial, Consejo Superior de Investigaciones Científicas, Campus UAB, Bellaterra, Spain;Institut d'Investigació en Intel.ligència Artificial, Consejo Superior de Investigaciones Científicas, Campus UAB, Bellaterra, Spain
Venue:
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
Year:
2005

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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.