2024-03-29T01:55:55Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/303502019-06-10T14:44:59Zcom_10261_106com_10261_4col_10261_485
Action rule induction from cause-effect pairs learned through robot-teacher interaction
Celaya, Enric
Torras, Carme
Wörgötter, Florentin
Agostini, Alejandro
Presentado a la International Conference on Cognitive Systems celebrada en Karlsruhe (Alemania) del 2 al 4 de abril de 2008.
In 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.
This work was supported by the project 'Perception, action & cognition through learning of object-action complexes.' (4915). This work is funded by the EU PACO-PLUS project FP6-2004-IST-4-
27657.
Peer Reviewed
2010-12-17T07:32:57Z
2010-12-17T07:32:57Z
2008
comunicación de congreso
http://purl.org/coar/resource_type/c_5794
CogSys 2008
http://hdl.handle.net/10261/30350
en
Publisher's version
http://www.cogsys2008.org/
open
Universität Karlsruhe