7 June 2013 Controlled English to facilitate human/machine analytical processing
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Controlled English is a human-readable information representation format that is implemented using a restricted subset of the English language, but which is unambiguous and directly accessible by simple machine processes. We have been researching the capabilities of CE in a number of contexts, and exploring the degree to which a flexible and more human-friendly information representation format could aid the intelligence analyst in a multi-agent collaborative operational environment; especially in cases where the agents are a mixture of other human users and machine processes aimed at assisting the human users. CE itself is built upon a formal logic basis, but allows users to easily specify models for a domain of interest in a human-friendly language. In our research we have been developing an experimental component known as the “CE Store” in which CE information can be quickly and flexibly processed and shared between human and machine agents. The CE Store environment contains a number of specialized machine agents for common processing tasks and also supports execution of logical inference rules that can be defined in the same CE language. This paper outlines the basic architecture of this approach, discusses some of the example machine agents that have been developed, and provides some typical examples of the CE language and the way in which it has been used to support complex analytical tasks on synthetic data sources. We highlight the fusion of human and machine processing supported through the use of the CE language and CE Store environment, and show this environment with examples of highly dynamic extensions to the model(s) and integration between different user-defined models in a collaborative setting.
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Dave Braines, Dave Braines, David Mott, David Mott, Simon Laws, Simon Laws, Geeth de Mel, Geeth de Mel, Tien Pham, Tien Pham, "Controlled English to facilitate human/machine analytical processing", Proc. SPIE 8758, Next-Generation Analyst, 875808 (7 June 2013); doi: 10.1117/12.2015907; https://doi.org/10.1117/12.2015907

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