1. osnaDocs
  2. Fachbereich 06 - Mathematik / Informatik / Physik
  3. FB06 - E-Dissertationen

Relevance-based Online Planning in Complex POMDPs

Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen:
https://osnadocs.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-202007173302
Open Access logo originally created by the Public Library of Science (PLoS)
Langanzeige der Metadaten
DC ElementWertSprache
dc.contributor.advisorProf. Dr. Joachim Hertzbergger
dc.creatorSaborío Morales, Juan Carlos-
dc.date.accessioned2020-07-17T10:08:18Z-
dc.date.available2020-07-17T10:08:18Z-
dc.date.issued2020-07-17T10:08:20Z-
dc.identifier.urihttps://osnadocs.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-202007173302-
dc.description.abstractPlanning under uncertainty is a central topic at the intersection of disciplines such as artificial intelligence, cognitive science and robotics, and its aim is to enable artificial agents to solve challenging problems through a systematic approach to decision-making. Some of these challenges include generating expectations about different outcomes governed by a probability distribution and estimating the utility of actions based only on partial information. In addition, an agent must incorporate observations or information from the environment into its deliberation process and produce the next best action to execute, based on an updated understanding of the world. This process is commonly modeled as a POMDP, a discrete stochastic system that becomes intractable very quickly. Many real-world problems, however, can be simplified following cues derived from contextual information about the relative expected value of actions. Based on an intuitive approach to problem solving, and relying on ideas related to attention and relevance estimation, we propose a new approach to planning supported by our two main contributions: PGS grants an agent the ability to generate internal preferences and biases to guide action selection, and IRE allows the agent to reduce the dimensionality of complex problems while planning online. Unlike existing work that improves the performance of planning on POMDPs, PGS and IRE do not rely on detailed heuristics or domain knowledge, explicit action hierarchies or manually designed dependencies for state factoring. Our results show that this level of autonomy is important to solve increasingly more challenging problems, where manually designed simplifications scale poorly.eng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Germany*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/de/*
dc.subjectPlanning under uncertaintyeng
dc.subjectPOMDP planningeng
dc.subjectMonte Carlo Tree Searcheng
dc.subject.ddc004 - Informatikger
dc.titleRelevance-based Online Planning in Complex POMDPseng
dc.typeDissertation oder Habilitation [doctoralThesis]-
thesis.locationOsnabrück-
thesis.institutionUniversität-
thesis.typeDissertation [thesis.doctoral]-
thesis.date2020-06-25-
orcid.creatorhttps://orcid.org/0000-0003-3625-0661-
dc.contributor.refereeProf. Dr. Marc Toussaintger
dc.subject.bk54.72 - Künstliche Intelligenzger
dc.subject.msc60-08 - Computational methodsger
dc.subject.ccsI.2.8 - Problem Solving, Control Methods, and Searchger
Enthalten in den Sammlungen:FB06 - E-Dissertationen

Dateien zu dieser Ressource:
Datei Beschreibung GrößeFormat 
thesis_saborio_morales.pdfPräsentationsformat1,02 MBAdobe PDF
thesis_saborio_morales.pdf
Miniaturbild
Öffnen/Anzeigen
Zur Kurzanzeige


Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons Creative Commons