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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)
Title:
Semantically Grounded Stream Reasoning Integrated with ROS
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 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Conference:
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 3-8, 2013 Tokyo, Japan
Publisher: IEEE conference proceedings
Series:
IEEE conference proceedings
Year of publ.:
2013
URI:
urn:nbn:se:liu:diva-95897
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-95897
ISI:
000331367406001
Subject category:
Computer Science
Project:
CADICS, CENIIT, eLLIT, CUAS, NFFP5
Abstract(en) :

High level reasoning is becoming essential to autonomous systems such as robots. Both the information available to and the reasoning required for such autonomous systems is fundamentally incremental in nature. A stream is a flow of incrementally available  information and reasoning over streams is called stream reasoning.  Incremental reasoning over streaming information is  necessary to support a number of important robotics functionalities such as situation awareness, execution monitoring, and decision making.

This paper presents a practical framework for semantically grounded temporal stream reasoning called DyKnow.  Incremental reasoning over  streams is achieved through efficient progression of temporal logical formulas. The reasoning is semantically grounded through a common ontology and a specification of the semantic content of streams relative to the ontology.  This allows the finding of relevant streams through semantic matching. By using semantic mappings between ontologies it is also possible to do semantic matching over multiple ontologies. The complete stream reasoning framework is integrated in the Robot  Operating System (ROS) thereby extending it with a stream reasoning capability.

Research funder:
eLLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from:
2013-08-07
Created:
2013-08-07
Last updated:
2014-04-14
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