LiU Electronic Press
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Author:
Heintz, Fredrik (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, The Institute of Technology)
Kvarnström, Jonas (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, The Institute of Technology) (APD)
Doherty, Patrick (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, The Institute of Technology)
Title:
Stream-Based Reasoning Support for Autonomous Systems
Department:
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab
Linköping University, The Institute of Technology
Publication type:
Conference paper (Refereed)
Language:
English
In:
Proceedings of the 19th European Conference on Artificial Intelligence (ECAI)
Publisher: IOS Press
Series:
Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389; 215
Year of publ.:
2010
URI:
urn:nbn:se:liu:diva-59944
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-59944
ISBN:
978-1-60750-605-8
ScopusID:
2-s2.0-77956034737
Subject category:
Engineering and Technology
SVEP category:
TECHNOLOGY
Abstract(en) :

For autonomous systems such as unmanned aerial vehicles to successfully perform complex missions, a great deal of embedded reasoning is required at varying levels of abstraction. To support the integration and use of diverse reasoning modules we have developed DyKnow, a stream-based knowledge processing middleware framework. By using streams, DyKnow captures the incremental nature of sensor data and supports the continuous reasoning necessary to react to rapid changes in the environment.  DyKnow has a formal basis and pragmatically deals with many of the architectural issues which arise in autonomous systems. This includes a systematic stream-based method for handling the sense-reasoning gap, caused by the wide difference in abstraction levels between the noisy data generally available from sensors and the symbolic, semantically meaningful information required by many highlevel reasoning modules. As concrete examples, stream-based support for anchoring and planning are presented.

Available from:
2010-09-30
Created:
2010-09-30
Last updated:
2013-08-29
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