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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bulitko, V.: Learning for adaptive real-time search. The Computing Research Rep. (CoRR): cs.DC/0407017 (2004)
Edelkamp, S., Eckerle, J.: New strategies in learning real time heuristic search. In: Proc. AAAI Workshop on On-Line Search, pp. 30–35 (1997)
Furcy, D., Koenig, S.: Speeding up the convergence of real-time search. In: Proc. AAAI, pp. 891–897 (2000)
Furcy, D., Koenig, S.: Combining two fast-learning real-time search algorithms yields even faster learning. In: Proc. 6th European Conference on Planning (2001)
Hernandez, C., Meseguer, P.: Improving convergence of lrta*(k). In: Proc. IJCAI Workshop on Planning and Learning in a Priori Unknown or Dynamic Domains, pp. 69–75 (2005)
Hernandez, C., Meseguer, P.: Lrta*(k). In: Proc. IJCAI, pp. 1238–1243 (2005)
Knight, K.: Are many reactive agents better than a few deliberative ones? In: Proc. 13th IJCAI, pp. 432–437 (1993)
Koenig, S.: A comparison of fast search methods for real-time situated agents. In: Proc. 3rd AAMAS, pp. 864–871 (2004)
Korf, R.E.: Real-time heuristic search. Artificial Intelligence 42(2-3), 189–211 (1990)
Shimbo, M., Ishida, T.: Controlling the learning process of real-time heuristic search. Artificial Intelligence 146(1), 1–41 (2003)
Thorpe, P.E.: A hybrid learning real-time search algorithm. Master’s thesis, Computer Science Dep., UCLA (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/11881216_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45914-9
Online ISBN: 978-3-540-45915-6
eBook Packages: Computer ScienceComputer Science (R0)