Transfer of Domain Knowledge in Plan Generation: Learning Goal-dependent Annulling Conditions for Actions
| dc.contributor.author | Siebers, Michael | |
| dc.date.accessioned | 2018-01-08T09:17:02Z | |
| dc.date.available | 2018-01-08T09:17:02Z | |
| dc.date.issued | 2014 | |
| dc.description.abstract | In this paper we present an approach to avoid dead-ends during automated plan generation. A first-order logic formula can be learned that holds in a state if the application of a specific action will lead to a dead-end. Starting from small problems within a problem domain examples of states where the application of the action will lead to a dead-end will be collected. The states will be generalized using inductive logic programming to a first-order logic formula. We will show how different notions of goal-dependence could be integrated in this approach. The formula learned will be used to speed-up automated plan generation. Furthermore, it provides insight into the planning domain under consideration. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11395 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 28, No. 1 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Automated planning | |
| dc.subject | Dead-end states | |
| dc.subject | Inductive logic programming | |
| dc.subject | Learning domain knowledge | |
| dc.title | Transfer of Domain Knowledge in Plan Generation: Learning Goal-dependent Annulling Conditions for Actions | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 38 | |
| gi.citation.startPage | 35 |