A hybrid reasoning mechanism for effective sensor selection for tasks

Geeth De Mel; Murat Sensoy; Wamberto Vasconcelos; Timothy J. Norman

doi:10.1016/j.engappai.2012.12.003

A hybrid reasoning mechanism for effective sensor selection for tasks

Geeth De Mel^*, Murat Sensoy, Wamberto Vasconcelos, Timothy J. Norman

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, we present Ontological Logic Programming (OLP), a novel approach that combines logic programming with ontological reasoning. OLP enables the use of ontological terms (i.e.; individuals, classes and properties) directly within logic programmes. The interpretation of these terms is delegated to an ontology reasoner during the interpretation of the programme. Unlike similar approaches, OLP makes use of the full capacity of both ontological reasoning and logic programming. We evaluate the computational properties of OLP in different settings and show that its performance can be significantly improved using caching mechanisms. We then introduce a comprehensive sensor-task selection solution based on OLP and discuss the benefits one can obtain by using OLP. The solution is based on a set of interlinking ontologies that capture the crucial domain knowledge of sensor networks. We then make use of OLP to create and manage complex concepts in the domain as well as to implement effective resource-task assignment algorithms, which compute appropriate resources for tasks such that they sufficiently cover the tasks needs. We compare the advantages of OLP with a knowledge-based set-covering mechanism for resource-task selection.

Original language	English
Pages (from-to)	873-887
Number of pages	15
Journal	Engineering Applications of Artificial Intelligence
Volume	26
Issue number	2
Early online date	7 Jan 2013
DOIs	https://doi.org/10.1016/j.engappai.2012.12.003
Publication status	Published - 1 Feb 2013

Bibliographical note

This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001. The views and conclusions in this document are those of the author(s) and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.

We acknowledge ITMAS 2011 as the forum in which the main ideas behind this paper were preliminary discussed.

Keywords

Knowledge-based resource selection
Logic Programming
Semantic web

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10.1016/j.engappai.2012.12.003Licence: Unspecified

Cite this

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title = "A hybrid reasoning mechanism for effective sensor selection for tasks",

abstract = "In this paper, we present Ontological Logic Programming (OLP), a novel approach that combines logic programming with ontological reasoning. OLP enables the use of ontological terms (i.e.; individuals, classes and properties) directly within logic programmes. The interpretation of these terms is delegated to an ontology reasoner during the interpretation of the programme. Unlike similar approaches, OLP makes use of the full capacity of both ontological reasoning and logic programming. We evaluate the computational properties of OLP in different settings and show that its performance can be significantly improved using caching mechanisms. We then introduce a comprehensive sensor-task selection solution based on OLP and discuss the benefits one can obtain by using OLP. The solution is based on a set of interlinking ontologies that capture the crucial domain knowledge of sensor networks. We then make use of OLP to create and manage complex concepts in the domain as well as to implement effective resource-task assignment algorithms, which compute appropriate resources for tasks such that they sufficiently cover the tasks needs. We compare the advantages of OLP with a knowledge-based set-covering mechanism for resource-task selection.",

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A hybrid reasoning mechanism for effective sensor selection for tasks

Abstract

Bibliographical note

Keywords

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