This position paper presents the initial designs for a novel functional, context-aware, case-based recommendation system to enhance the current intelligence capability of military intelligence analysts. Its central objective is to support these analysts during the collection, processing, and analysis phases of the intelligence cycle through load minimization and improved human-machine synergy. This involves sense-making from both explicit and implicit contextual information with a nexus of technologies and processes, including software modelling and simulation, meta-level modelling and recommendation, and psycho-physiological modelling and monitoring. The proposed system, RECON, will contribute a new architecture based on five core components: i) brain-computer interfaces, ii) humancomputer interaction, iii) data, iv) context, and v) case-based recommendation. Together with human analysts these form the basis of an adaptive system that will advance toward a powerful future intelligence analysis capability.

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Keywords Adaptive human-machine systems, Brain-computer interfaces, Case-based recommendation, Context awareness, Information relevance, Modelling and simulation
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Conference 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
Ross, W. (William), Morris, A. (Alexis), Ulieru, M, & Guyard, A.B. (Alexandre Bergeron). (2013). RECON: An adaptive human-machine system for supporting intelligence analysis. Presented at the 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013. doi:10.1109/SMC.2013.138