Intelligent Monitoring of Intelligence Data

Lead Research Organisation: Aberystwyth University
Department Name: Computer Science

Abstract

In the wake of recent terrorist atrocities, intelligence experts have commented that failures in detecting terrorist activities are not so much due to a lack of data, as they are due to difficulties in relating and interpreting the available intelligence. An automated tool for monitoring and interpreting intelligence data will provide a helpful means for intelligence analysts to consider emerging scenarios of plausible terrorist attacks, thereby offering useful assistance in devising and deploying preventive measures against such possibilities.While conventional knowledge-based systems, such as rule-based and case-based reasoners, have useful applications for common crime detection, their scope is restricted to either the situations foreseen or those resulting from previously encountered cases. To devise a robust monitoring system that is capable of identifying many variations on a given type of terrorist activity, this project will employ a model-based approach to scenario generation. The main potential of such work is its ability to construct automatically many variations of a given type of scenario from a relatively small knowledge base, by dynamically combining reusable scenario fragments. The proposed work involves a number of research activities that are entirely new. In particular, the resulting compositional modelling methods will be the first which are able to work with multiple scenarios at one time, the first which can handle ill-defined concepts, and the first to incorporate qualitative preference handling. Also, the application of these methods to aid in monitoring intelligence data is itself highly novel. This research will provide theoretical foundations to develop software systems that will aid anti-terrorist intelligence analysts in considering as widely as possible the range of emerging scenarios that may reflect organised terrorist activities. The work will have the ability of linking up seemingly distinct and unrelated intelligence data and associating such data with logically inferred and justified possible scenarios. The multi-disciplinary team assembled for this project involves leading researchers in artificial intelligence, and in counter-terrorism and intelligence studies, offering a very strong combination to undertake the work.

Publications

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Boongoen T (2010) Disclosing false identity through hybrid link analysis in Artificial Intelligence and Law

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Boongoen T (2010) Nearest-neighbor guided evaluation of data reliability and its applications. in IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society

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Boongoen T (2011) Extending Data Reliability Measure to a Filter Approach for Soft Subspace Clustering. in IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society

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BOONGOEN T (2011) FUZZY QUALITATIVE LINK ANALYSIS FOR ACADEMIC PERFORMANCE EVALUATION in International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

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Li S (2008) Random fuzzy delayed renewal processes in Soft Computing

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Qiang Shen (2008) Modeling Random Fuzzy Renewal Reward Processes in IEEE Transactions on Fuzzy Systems

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Shen Q (2011) A Credibilistic Approach to Assumption-Based Truth Maintenance in IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans

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Shen Q (2012) Fuzzy Orders-of-Magnitude-Based Link Analysis for Qualitative Alias Detection in IEEE Transactions on Knowledge and Data Engineering

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Shen Q (2010) Risk assessment of serious crime with fuzzy random theory in Information Sciences