Abstract
A* is often described as being 'optimal', in that it expands the minimum number of unique nodes. But, A* may generate many extra nodes which are never expanded. This is a performance loss, especially when the branching factor is large. Partial Expansion A* (PEA*) (Yoshizumi, Miura, and Ishida 2000) addresses this problem when expanding a node, n, by generating all the children of n but only storing children with the same f-cost as n. n is re-inserted into the OPEN list, but with the f-cost of the next best child. This paper introduces an enhanced version of PEA* (EPEA*). Given a priori domain knowledge, EPEA* generates only the children with the same f-cost as the parent. EPEA* is generalized to its iterative-deepening variant, EPE-IDA*. For some domains, these algorithms yield substantial performance improvements. State-of-the-art results were obtained for the pancake puzzle and for some multi-agent pathfinding instances. Drawbacks of EPEA* are also discussed.
| Original language | American English |
|---|---|
| Pages | 471-477 |
| Number of pages | 7 |
| State | Published - 1 Jan 2012 |
| Event | 26th AAAI Conference on Artificial Intelligence, AAAI 2012 - Toronto, Canada Duration: 22 Jul 2012 → 26 Jul 2012 |
Conference
| Conference | 26th AAAI Conference on Artificial Intelligence, AAAI 2012 |
|---|---|
| Country/Territory | Canada |
| City | Toronto |
| Period | 22/07/12 → 26/07/12 |
All Science Journal Classification (ASJC) codes
- Artificial Intelligence