Abstract
Symbolic PDBs and Merge-and-Shrink (M&S) are two approaches to derive admissible heuristics for optimal planning. We present a combination of these techniques, Symbolic Merge-and-Shrink (SM&S), which uses M&S abstractions as a relaxation criterion for a symbolic backward search. Empirical evaluation shows that SM&S has the strengths of both techniques deriving heuristics at least as good as the best of them for most domains.
| Original language | English |
|---|---|
| Title of host publication | IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence |
| Number of pages | 7 |
| Publication date | 2013 |
| Pages | 2394-2400 |
| ISBN (Print) | 9781577356332 |
| Publication status | Published - 2013 |
| Externally published | Yes |
| Event | 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China Duration: 3 Aug 2013 → 9 Aug 2013 |
Conference
| Conference | 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 |
|---|---|
| Country/Territory | China |
| City | Beijing |
| Period | 03/08/2013 → 09/08/2013 |
| Sponsor | International Joint Conferences on Artificial, Intelligence (IJCAI), Artificial Intelligence Journal, National Science Foundation (NSF), National Natural Science Foundation of China |
| Series | IJCAI International Joint Conference on Artificial Intelligence |
|---|---|
| ISSN | 1045-0823 |
Bibliographical note
Copyright:Copyright 2014 Elsevier B.V., All rights reserved.
Keywords
- Artificial Intelligence (AI)
- Planning and scheduling