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Proceedings Paper

Collaborative human-machine analysis using a controlled natural language
Author(s): David H. Mott; Donald R. Shemanski; Cheryl Giammanco; Dave Braines
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Paper Abstract

A key aspect of an analyst's task in providing relevant information from data is the reasoning about the implications of that data, in order to build a picture of the real world situation. This requires human cognition, based upon domain knowledge about individuals, events and environmental conditions. For a computer system to collaborate with an analyst, it must be capable of following a similar reasoning process to that of the analyst. We describe ITA Controlled English (CE), a subset of English to represent analyst's domain knowledge and reasoning, in a form that it is understandable by both analyst and machine. CE can be used to express domain rules, background data, assumptions and inferred conclusions, thus supporting human-machine interaction. A CE reasoning and modeling system can perform inferences from the data and provide the user with conclusions together with their rationale. We present a logical problem called the "Analysis Game", used for training analysts, which presents “analytic pitfalls” inherent in many problems. We explore an iterative approach to its representation in CE, where a person can develop an understanding of the problem solution by incremental construction of relevant concepts and rules. We discuss how such interactions might occur, and propose that such techniques could lead to better collaborative tools to assist the analyst and avoid the “pitfalls”.

Paper Details

Date Published: 15 May 2015
PDF: 15 pages
Proc. SPIE 9499, Next-Generation Analyst III, 94990J (15 May 2015); doi: 10.1117/12.2180121
Show Author Affiliations
David H. Mott, IBM United Kingdom Ltd. (United Kingdom)
Donald R. Shemanski, The Pennsylvania State Univ. (United States)
Cheryl Giammanco, U.S. Army Research Lab. (United States)
Dave Braines, IBM United Kingdom Ltd. (United Kingdom)

Published in SPIE Proceedings Vol. 9499:
Next-Generation Analyst III
Barbara D. Broome; Timothy P. Hanratty; David L. Hall; James Llinas, Editor(s)

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