No abstract

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.

A number of studies have explored the dynamics of opinion change among interacting knowledge workers, using different modeling techniques. We are particularly interested in the transition from cognitive convergence (a positive group phenomenon) to collapse (which can lead to overlooking critical information). This paper extends previous agent-based studies of this subject in two directions. First, we allow agents to belong to distinct social groups and explore the effect of varying degrees of within-group affinity. Second, we provide exogenous drivers of agent opinion in the form of a dynamic set of documents that they may query. We exhibit a metastable configuration of this system with three distinct phases, and develop an operational metric for distinguishing convergence from collapse in the final phase. Then we use this metric to explore the system's dynamics, over the space defined by social affinity and precision of queries against documents, and under a range of different functions for the influence that an interaction partner has on an agent.

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Washington Headquarters Service, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC 20503. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY)APR 09 REPORT TYPE Final DATES COVERED (From -To)Dec 07 -Jan 09 TITLE AND SUBTITLE PROACTIVE INTELLIGENCE (PAINT) SIMULATED EXPLORATION OF EXECUTABLE DESIGN STRATEGIES (SEEDS)5a PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Techteam Government Solutions, Inc. 3863 Centerview Drive, Suite 150 Chantilly, VA 20151-3287 PERFORMING ORGANIZATION REPORT NUMBERN/A SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)AFRL/RIED 525 Brooks Rd. Rome NY 13441-4505 SPONSOR/MONITOR'S ACRONYM(S)N/A SPONSORING/MONITORING AGENCY REPORT NUMBER AFRL-RI-RS-TR-2009-102 DISTRIBUTION AVAILABILITY STATEMENT APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. PA# 88ABW-2009-1389 SUPPLEMENTARY NOTES ABSTRACTThe ProActive Intelligence(PAINT) ACCOMPLISHMENTSIn the work package executed with the available funding in the Pro Active Intelligence(PAINT) Simulated Exploration of Executable Design Strategies(SEEDS) project we first defined the overall systems architecture for the creation of robust, complex, and optimized probes based on a generate and test approach, then we specified the architecture and processes within the Possible World Generator, our key component for testing and evaluating probe candidates, and finally, we implemented and demonstrated an illustrationof-concept interactive prototype that shows the primary interactions of the key components of the Possible World Generator. PAINT SEEDS ARCHITECTUREIn the PAINT SEEDS architecture we define four main components supporting a generateand-test strategy to search for robust, complex, and optimized probes:1. A low-dimensional polyagent simulation generating multiple futures concurrently, with the model derived from the high-dimensional models of the other PAINT performers, structured into a leadership model with target models representing the goals of individual leadership actors, and a pathway model representing the constraints of technology development 2. A trajectory evaluation component that evaluates the effect of the execution of a particular probe candidate on the predicted evolution of the system under analysis and our ability to discern or influence the intent and plans of the leadership in terms of the specific outcome of the technology development3. An adaptive distributed search infrastructure that guides the gene...