Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Feature dynamic Bayesian networks

Simple item page
dc.contributor.authorHutter, Marcus
dc.coverage.spatialArlington USA
dc.date.accessioned2015-12-10T22:43:29Z
dc.date.createdMarch 6-9 2009
dc.date.issued2009
dc.date.updated2016-02-24T11:45:00Z
dc.description.abstractFeature Markov Decision Processes (ΦMDPs) [Hut09] are well-suited for learning agents in general environments. Nevertheless, unstructured (Φ)MDPs are limited to relatively simple environments, Structured MDPs like Dynamic Bayesian Networks (DBNs) are us
dc.identifier.isbn9789078677246
dc.identifier.urihttp://hdl.handle.net/1885/58189
dc.publisherAtlantis Press
dc.relation.ispartofseriesConference on Artificial General Intelligence (AGI 2009)
dc.rightsCopyright Information: © Atlantis Press and the Author(s). This article is distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work i
dc.sourceAdvances in Intelligent Systems Research: Proceedings of the 2nd Conference on Artificial General Intelligence (AGI 2009)
dc.source.urihttp://www.atlantis-press.com/publications/aisr/AGI-09
dc.subjectKeywords: Building blockes; Cost criteria; Dynamic Bayesian network; Dynamic bayesian networks; General learning; Learning agents; Markov Decision Processes; Primary contribution; Real-world problem; Distributed parameter networks; Feature extraction; Inference eng
dc.titleFeature dynamic Bayesian networks
dc.typeConference paper
local.bibliographicCitation.lastpage73
local.bibliographicCitation.startpage67
local.contributor.affiliationHutter, Marcus, College of Engineering and Computer Science, ANU
local.contributor.authoruidHutter, Marcus, u4350841
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationu8803936xPUB431
local.identifier.doi10.2991/agi.2009.6
local.identifier.scopusID2-s2.0-77955184282
local.type.statusPublished Version

Downloads

Original bundle

Now showing 1 - 2 of 2
Thumbnail Image
Name:
01_Hutter_Feature_dynamic_Bayesian_2009.pdf
Size:
411.76 KB
Format:
Adobe Portable Document Format
Download
Thumbnail Image
Name:
02_Hutter_Feature_dynamic_Bayesian_2009.pdf
Size:
36.89 KB
Format:
Adobe Portable Document Format
Download

Collections

ANU Research Publications

DSpace software copyright © 2002-2026 LYRASIS