Unclassified//For Official Use Only 1 RAPID: Representation and Analysis of Probabilistic Intelligence Data Carnegie Mellon University PI : Prof. Jaime G. Carbonell / / (412) Dr. Eugene Fink / / (412) Dr. Anatole Gershman / / (412) DYNAMiX Technologies POC: Dr. Ganesh Mani / / (412) Mr. Dwight Dietrich / / (724) PAINT

Unclassified//For Official Use Only 2 RAPID: Product RAPID is a software system for the analysis of dynamically evolving intelligence, designed to help analysts: –Draw conclusions from available intelligence (including uncertain and missing data), identifying potentially surprising developments –Answer queries –Formulate and assess hypotheses –Identify critical uncertainties –Develop strategies for proactive collection of additional data to resolve critical uncertainties

Unclassified//For Official Use Only 3 RAPID Contributions Rapid will provide the following PAINT functions: –Automatic discovery of causal relationships (1) –Fast probabilistic integration of all evidence (3) –Identification of critical uncertainties and surprises (4) –Development of pro-active cost/benefit weighted intelligence gathering plans (4) 1 Feedback Data Strategy Generation & Exploration Dynamic Simulation Models Response Options 324

Unclassified//For Official Use Only 4 Management and analysis of massive amounts of structured uncertain data, including intelligence reports, unclassified data, analyst opinions, inference rules, hypotheses, and data-collection plans. RAPID’s technology includes: RAPID: Technology Novel representation of massive amounts of uncertain data, which supports fast retrieval and inferences Scalable inference mechanism for reasoning about uncertain intelligence Application of predictive Markov models to analyze alternative hypotheses and possible future developments Construction of optimized intelligence collection plans Integrated graphical user interface for collaboration between the system and human analysts

Unclassified//For Official Use Only 5 RAPID: Technology Knowledge base Adversarial goals Inference rules Task hierarchy Uncertain situation assessment Massive databases, including both certain and uncertain data Indexing and retrieval Fast retrieval of exact and approximate matches Automated construction of new rules and Markov decision trees Learning of new knowledge Massive new intelligence PROACTIVE INTELLIGENCE CONTROL Identification of critical uncertainties Contingency analysis Adversarial search Tools for manual modification of the available knowledge Knowledge editing Analyst GUI Hypotheses, conclusions, and data-collection plans Markov decision trees Explanation of inferences General intelligence collection Proactive intelligence collection Hypotheses, conclusions, and data-collection plans Massive new intelligence Distribution model:Executable code and documentation System requirements:High-end Windows desktop computer

Unclassified//For Official Use Only 6 Rapid Inputs/Outputs Rapid inputs from other PAINT components and analysts: –Available data and its certainty –Hypotheses about unknown factors and their certainty –Intelligence priorities and analyst feedback –Responses to RAPID-generated probes Rapid outputs will include: –Prioritized lists of hypotheses about unknown factors and their certainties –Cost/benefit weighted plans for pro-active intelligence gathering We are flexible regarding the specific structure of RAPID inputs and outputs

Unclassified//For Official Use Only 7 RAPID Domain Acquisition of strategic technological capabilities by countries and organizations through tracking of: –known and emerging technologies with potential military applications –specialists in these technologies, their affiliations, professional networks, publications, speeches, conference attendance, etc. –companies and R&D centers interested in these technologies, their affiliations, ownership, alliances, supply and distribution networks, and potential intentions –pronouncements by business and political leaders regarding strategic technologies –financial information related to these technologies Example: Iran’s plans with respect to nano-technology

Unclassified//For Official Use Only 8 RAPID: Evaluation We will compare the productivity of analysts using RAPID with that of analysts who perform the same tasks using commercially available tools. We will measure the following main factors to evaluate the performance of analysts: Number of high-level tasks completed within the experiment time frame Accuracy of hypothesis evaluation Number and relevance of identified patterns Effectiveness and costs of data-collection plans We will view the proposed work as success if RAPID consistently outperforms the available off-the-shelf tools in all four main factors, the performance difference for each factor is statistically significant, and analysts report the overall positive experience of using the system.

Unclassified//For Official Use Only 9 RAPID: Availability Version 1: Scalable probabilistic matching of uncertain intelligence data July 2008 Version 2: Discrimination among competing hypotheses based on fast probabilistic inference; basic proactive identification of critical uncertainties July 2009 Version 3: Advanced proactive-intelligence planning; learning of hypotheses and inference rules; graphical user interface July 2010 Version 4: Analysis of contingencies and adversarial goals; explanation of inferences July 2011 Version 5: Full-featured delivereable systemDec All versions of RAPID will demonstrate all capabilities – with increasing functionality over time, with primary emphasis on:

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