Learning probabilistic action models from interpretation transitions

All of RECERCAT

To access the full text documents, please follow this link: http://hdl.handle.net/2117/84962

Title:	Learning probabilistic action models from interpretation transitions
Author:	Martínez Martínez, David; Ribeiro, Tony; Inoue, Katsumi; Alenyà Ribas, Guillem; Torras, Carme
Other authors:	Institut de Robòtica i Informàtica Industrial; Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
Abstract:	Probabilistic planners are very flexible tools that provide good solutions for difficult tasks. However, they rely on a model of the domain and actions, which they have difficulties to learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learn
Abstract:	Peer Reviewed
Subject(s):	-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial -learning (artificial intelligence) -planning (artificial intelligence) -uncertainty handling. -Classificació INSPEC::Cybernetics::Artificial intelligence::Learning (artificial intelligence)
Rights:	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Document type:	Article - Published version Conference Object
Share: