English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/30350
Share/Impact:
Statistics
logo share SHARE   Add this article to your Mendeley library MendeleyBASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

Action rule induction from cause-effect pairs learned through robot-teacher interaction

AuthorsCelaya, Enric ; Torras, Carme ; Wörgötter, Florentin; Agostini, Alejandro
Issue Date2008
PublisherUniversität Karlsruhe
CitationCogSys 2008
AbstractIn this work we propose a decision-making system that efficiently learns behaviors in the form of rules using natural human instructions about cause-effect relations in currently observed situations, avoiding complicated instructions and explanations of long-run action sequences and complete world dynamics. The learned rules are represented in a way suitable to both reactive and deliberative approaches, which are thus smoothly integrated. Simple and repetitive tasks are resolved reactively, while complex tasks would be faced in a more deliberative manner using a planner module. Human interaction is only required if the system fails to obtain the expected results when applying a rule, or fails to resolve the task with the knowledge acquired so far.
DescriptionPresentado a la International Conference on Cognitive Systems celebrada en Karlsruhe (Alemania) del 2 al 4 de abril de 2008.
Publisher version (URL)http://www.cogsys2008.org/
URIhttp://hdl.handle.net/10261/30350
Appears in Collections:(IRII) Comunicaciones congresos
Files in This Item:
File Description SizeFormat 
Action rule induction.pdf526,13 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.