Robust planning with (L)RTDP
| dc.contributor.author | Buffet, Olivier | en |
| dc.contributor.author | Aberdeen, Douglas | en |
| dc.date.accessioned | 2025-05-27T21:20:27Z | |
| dc.date.available | 2025-05-27T21:20:27Z | |
| dc.date.issued | 2005 | en |
| dc.description.abstract | Stochastic Shortest Path problems (SSPs), a subclass of Markov Decision Problems (MDPs), can be efficiently dealt with using Real-Time Dynamic Programming (RTDP). Yet, MDP models are often uncertain (obtained through statistics or guessing). The usual approach is robust planning: searching for the best policy under the worst model. This paper shows how RTDP can be made robust in the common case where transition probabilities are known to lie in a given interval. | en |
| dc.description.status | Peer-reviewed | en |
| dc.format.extent | 6 | en |
| dc.identifier.other | Scopus:78650598019 | en |
| dc.identifier.other | ARIES:MigratedxPub13789 | en |
| dc.identifier.uri | http://www.scopus.com/inward/record.url?scp=78650598019&partnerID=8YFLogxK | en |
| dc.identifier.uri | https://dspace-test.anu.edu.au/handle/1885/733758773 | |
| dc.language.iso | en | en |
| dc.relation.ispartofseries | 19th International Joint Conference on Artificial Intelligence, IJCAI 2005 | en |
| dc.source | IJCAI International Joint Conference on Artificial Intelligence | en |
| dc.title | Robust planning with (L)RTDP | en |
| dc.type | Conference paper | en |
| local.bibliographicCitation.lastpage | 1219 | en |
| local.bibliographicCitation.startpage | 1214 | en |
| local.contributor.affiliation | Buffet, Olivier; School of Computing, ANU College of Systems and Society, The Australian National University | en |
| local.contributor.affiliation | Aberdeen, Douglas; School of Computing, ANU College of Systems and Society, The Australian National University | en |
| local.identifier.pure | f0099556-8e91-4e13-80fc-5806c8d4ab12 | en |
| local.type.status | Published | en |