Human-machine conversations to support multi-agency missions

Preece, Alun David ORCID: https://orcid.org/0000-0003-0349-9057, Braines, Dave, Pizzocaro, Diego ORCID: https://orcid.org/0000-0003-1976-8805 and Parizas, Christos 2014. Human-machine conversations to support multi-agency missions. ACM SIGMOBILE Mobile Computing and Communications Review 18 (1) , pp. 75-84. 10.1145/2581555.2581568

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Abstract

In domains such as emergency response, environmental monitoring, policing and security, sensor and information networks are deployed to assist human users across multiple agencies to conduct missions at or near the 'front line'. These domains present challenging problems in terms of human-machine collaboration: human users need to task the network to help them achieve mission objectives, while humans (sometimes the same individuals) are also sources of mission-critical information. We propose a natural language-based conversational approach to supporting humanmachine working in mission-oriented sensor networks. We present a model for human-machine and machine-machine interactions in a realistic mission context, and evaluate the model using an existing surveillance mission scenario. The model supports the flow of conversations from full natural language to a form of Controlled Natural Language (CNL) amenable to machine processing and automated reasoning, including high-level information fusion tasks. We introduce a mechanism for presenting the gist of verbose CNL expressions in a more convenient form for human users. We show how the conversational interactions supported by the model include requests for expansions and explanations of machine-processed information.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Association for Computing Machinery
ISSN:	1559-1662
Date of First Compliant Deposit:	30 March 2016
Last Modified:	12 Jun 2023 19:36
URI:	https://orca.cardiff.ac.uk/id/eprint/57509

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