A Multi-Agent Context-Management System for RECON Intelligence Analysis

Adaptive systems require technologies to enable high synchronicity between its users and their unfolding situation dynamics, in concert with system response actions. To be effective, a multi-dimensional view of context must be considered and incorporated. This work advances the development of such a system for RECON, an initiative to support intelligence analysts with a novel context-management and case-based recommendation capability. The central concepts involved in the management of explicit and implicit contexts are presented and are developed into a novel multi-agent approach. In particular a new context-sensitive cognitive model and a community of expert service-oriented agents are proposed to facilitate and improve system adaptations to user-specific, situational, and system states. These designs pave the way towards future developments and experiments in improving human–machine interaction with adaptive context-management systems.