An ontology-based multi-level robot architecture for learning from experiences
Rockel, S, Neumann, B, Zhang, J et al. (14 more authors)
(2013)
An ontology-based multi-level robot architecture for learning from experiences.
In:
Designing Intelligent robots: Reintegrating AI II. AAAI Spring Symposium - Technical Report.
AAAI Spring Symposium 2013, 25-27 Mar 2013, Palo Alto, California, USA.
AAAI Press
, 52 - 57.
ISBN 9781577356011
Abstract
One way to improve the robustness and flexibility of robot performance is to let the robot learn from its experiences. In this paper, we describe the architecture and knowledge-representation framework for a service robot being developed in the EU project RACE, and present examples illustrating how learning from experiences will be achieved. As a unique innovative feature, the framework combines memory records of low-level robot activities with ontology-based high-level semantic descriptions.
Metadata
Authors/Creators:
Rockel, S
Neumann, B
Zhang, J
Dubba, KSR
Cohn, AG
Konečný, Š
Mansouri, M
Pecora, F
Saffiotti, A
Günther, M
Stock, S
Hertzberg, J
Tomé, AM
Pinho, A
Seabra Lopes, L
Von Riegen, S
Hotz, L
Copyright, Publisher and Additional Information:
(c) 2013, AAAI. This is an author produced version of a paper published in Designing Intelligent robots:
Reintegrating AI II. AAAI Spring Symposium - Technical Report. Uploaded with permission from the publisher.
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