Abstract
The use of abstraction to speedup problem solving is ubiquitous in AI, especially in the field of heuristic search where abstraction has proven a crucial technique for creating highly accurate memory-based heuristics known as pattern databases (PDBs). While PDBs are intrinsically based on problem abstractions [1], the converse is not necessarily true, and this suggests that abstraction should play a much bigger role than simply improving the quality of the heuristic.
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
References
Zhou, R., Hansen, E.: Space-efficient memory-based heuristics. In: AAAI-04. Proceedings of the 19th National Conference on Artificial Intelligence, pp. 677–682 (2004)
Zhou, R., Hansen, E.: Structured duplicate detection in external-memory graph search. In: AAAI-04. Proceedings of the 19th National Conference on Artificial Intelligence, pp. 683–688 (2004)
Zhou, R., Hansen, E.: External-memory pattern databases using structured duplicate detection. In: AAAI-05. Proc. of the 20th National Conference on Artificial Intelligence, pp. 1398–1405 (2005)
Zhou, R., Hansen, E.: Domain-independent structured duplicate detection. In: AAAI 2006. Proc. of the 21st National Conference on Artificial Intelligence, pp. 1082–1087 (2006)
Zhou, R., Hansen, E.: Parallel structured duplicate detection. In: AAAI 2007. Proc. of the 22nd National Conference on Artificial Intelligence (to appear)
Zhou, R., Hansen, E.: Breadth-first heuristic search. Artificial Intelligence 170, 385–408 (2006)
Zhou, R., Hansen, E.: Breadth-first heuristic search. In: ICAPS 2004. Proceedings of the 14th International Conference on Automated Planning and Scheduling, pp. 92–100 (2004)
Zhou, R., Hansen, E.: Beam-stack search: Integrating backtracking with beam search. In: ICAPS 2005. Proceedings of the 15th International Conference on Automated Planning and Scheduling, pp. 90–98 (2005)
Zhou, R., Hansen, E.: Edge partitioning in external-memory graph search. In: IJCAI 2007. Proc. of the 20th International Joint Conference on Artificial Intelligence, pp. 2410–2416 (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, R. (2007). Leveraging Graph Locality Via Abstraction. In: Miguel, I., Ruml, W. (eds) Abstraction, Reformulation, and Approximation. SARA 2007. Lecture Notes in Computer Science(), vol 4612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73580-9_42
Download citation
DOI: https://doi.org/10.1007/978-3-540-73580-9_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73579-3
Online ISBN: 978-3-540-73580-9
eBook Packages: Computer ScienceComputer Science (R0)