Name
Abstract | ||
|---|---|---|
Knowledge-based planning methods offer benefits over classical techniques, but they are time consuming and costly to construct. There has been research on learning plan knowledge from search, but this can take substantial computer time and may even fail to find solutions on complex tasks. Here we describe another approach that observes sequences of operators taken from expert solutions to problems and learns hierarchical task networks from them. The method has similarities to previous algorithms for explanation-based learning, but differs in its ability to acquire hierarchical structures and in the generality of learned conditions. These increase the method's capability to transfer learned knowledge to other problems and supports the acquisition of recursive procedures. After presenting the learning algorithm, we report experiments that compare its abilities to other techniques on two planning domains. In closing, we review related work and directions for future research. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1143844.1143928 | ICML |
Keywords | Field | DocType |
knowledge-based planning method,substantial computer time,hierarchical task network,hierarchical structure,planning domain,classical technique,plan knowledge,time consuming,explanation-based learning,knowledge base | Multi-task learning,Hierarchical task network,Learning plan,Computer science,Artificial intelligence,Operator (computer programming),Generality,Machine learning,Recursion | Conference |
ISBN | Citations | PageRank |
1-59593-383-2 | 43 | 1.71 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Negin Nejati | 1 | 94 | 5.95 |
| Pat Langley | 2 | 6471 | 1307.64 |
| Tolga Konik | 3 | 50 | 2.57 |