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.
Vol. SS-13-.
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
Item Type:
Proceedings Paper
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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