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.
Supported by the Spanish REPLI project TIC-2002-04470-C03-03.
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© 2006 Springer-Verlag Berlin Heidelberg
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Hernández, C., Meseguer, P. (2006). Propagating Updates in Real-Time Search: HLRTA*(k). In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds) Current Topics in Artificial Intelligence. CAEPIA 2005. Lecture Notes in Computer Science(), vol 4177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881216_40
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DOI: https://doi.org/10.1007/11881216_40
Publisher Name: Springer, Berlin, Heidelberg
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