Self-Organizing Integration of Competing Reasoners for Information Matching

Self-organizing systems are robust, scalable, adaptive to a changing environment, and tolerant to noise and incomplete or conflicting information. These are the requirements for our information matching system (IMS) that organizes models of document contents and user interest in an abstract information space by relevance to provide any-time recommendations of other users (for collaboration) or documents (for information gathering) to intelligence analysts. In this report on research-in-progress, we present a plug-and-play integration architecture for multiple and possibly competing modelers of arbitrary (text, audio, video, etc) document contents that influence the emerging arrangement of document and user models. The contributions of these modelers are numerical similarity statements that specify attractive or repulsive forces, which guide the ongoing rearrangement of the current set of models. This self-organizing force-based arrangement process adjusts dynamically to changes in the document set or shifting user interest. Our paper also discusses related research, initial experiments that indicate satisfactory system-level behavior, and an upcoming evaluation exercise with actual users.