Tasking and sharing sensing assets using controlled natural language

Preece, Alun David ORCID: https://orcid.org/0000-0003-0349-9057, Pizzocaro, Diego ORCID: https://orcid.org/0000-0003-1976-8805, Braines, David and Mott, David 2012. Tasking and sharing sensing assets using controlled natural language. Presented at: Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR III, Baltimore, ML, 23 April 2012. Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE). SPIE SPIE, p. 838905. 10.1117/12.918839

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Abstract

We introduce an approach to representing intelligence, surveillance, and reconnaissance (ISR) tasks at a relatively high level in controlled natural language. We demonstrate that this facilitates both human interpretation and machine processing of tasks. More specically, it allows the automatic assignment of sensing assets to tasks, and the informed sharing of tasks between collaborating users in a coalition environment. To enable automatic matching of sensor types to tasks, we created a machine-processable knowledge representation based on the Military Missions and Means Framework (MMF), and implemented a semantic reasoner to match task types to sensor types. We combined this mechanism with a sensor-task assignment procedure based on a well-known distributed protocol for resource allocation. In this paper, we re-formulate the MMF ontology in Controlled English (CE), a type of controlled natural language designed to be readable by a native English speaker whilst representing information in a structured, unambiguous form to facilitate machine processing. We show how CE can be used to describe both ISR tasks (for example, detection, localization, or identication of particular kinds of object) and sensing assets (for example, acoustic, visual, or seismic sensors, mounted on motes or unmanned vehicles). We show how these representations enable an automatic sensor-task assignment process. Where a group of users are cooperating in a coalition, we show how CE task summaries give users in the eld a high-level picture of ISR coverage of an area of interest. This allows them to make ecient use of sensing resources by sharing tasks.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Publisher:	SPIE
ISSN:	0277-786X
Last Modified:	11 Jun 2023 01:18
URI:	https://orca.cardiff.ac.uk/id/eprint/31667

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