Learning High-Level Behaviors From Demonstration Through Semantic Networks
2012 (English)In: Proceedings of 4th International Conference on Agents and Artificial Intelligence, 2012, 419-426 p.Conference paper (Refereed)
In this paper we present an approach for high-level behavior recognition and selection integrated with alow-level controller to help the robot to learn new skills from demonstrations. By means of SemanticNetwork as the core of the method, the robot gains the ability to model the world with concepts and relatethem to low-level sensory-motor states. We also show how the generalization ability of Semantic Networkscan be used to extend learned skills to new situations.
Place, publisher, year, edition, pages
2012. 419-426 p.
Learning from Demonstration, High-Level Behaviors, Semantic Networks, Robot Learning
Research subject Computing Science
IdentifiersURN: urn:nbn:se:umu:diva-52233OAI: oai:DiVA.org:umu-52233DiVA: diva2:501519
4th International Conference on Agents and Artificial Intelligence (ICAART), 6-8 February 2012, Vilamoura, Algarve, Portugal
FunderEU, FP7, Seventh Framework Programme, 238486