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RAPID: Representation and Analysis of Probabilistic Intelligence Data

PAINT. RAPID: Representation and Analysis of Probabilistic Intelligence Data. Carnegie Mellon University PI : Prof. Jaime G. Carbonell / jgc@cs.cmu.edu / (412) 268-7279 Dr. Eugene Fink / e.fink@cs.cmu.edu / (412) 268-6593 Dr. Anatole Gershman / anatoleg@cs.cmu.edu / (412) 268-8259

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RAPID: Representation and Analysis of Probabilistic Intelligence Data

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  1. PAINT RAPID:Representation and Analysis ofProbabilistic Intelligence Data Carnegie Mellon University PI : Prof. Jaime G. Carbonell / jgc@cs.cmu.edu / (412) 268-7279 Dr. Eugene Fink / e.fink@cs.cmu.edu / (412) 268-6593 Dr. Anatole Gershman / anatoleg@cs.cmu.edu / (412) 268-8259 DYNAMiX Technologies POC: Dr. Ganesh Mani / gmani@dynamixtechnologies.com / (412) 401-0121 Mr. Dwight Dietrich / ddietrich@dynamixtechnologies.com / (724) 940-4304

  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

  3. 1 3 2 4 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) Strategy Generation & Exploration Response Options Data Dynamic Simulation Models Feedback

  4. RAPID: Technology 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: • 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

  5. Taskhierarchy Inferencerules RAPID: Technology PROACTIVE INTELLIGENCE CONTROL Generalintelligencecollection Identification ofcritical uncertainties Contingency analysis Adversarialsearch Explanationof inferences Uncertain situation assessment Analyst GUI Hypotheses,conclusions, and data-collection plans Hypotheses,conclusions, and data-collection plans Massive newintelligence Massive newintelligence Massive databases, includingboth certain and uncertain data Proactiveintelligencecollection Indexing and retrieval Knowledge base Fast retrieval of exactand approximate matches Adversarial goals Markov decision trees Knowledge editing Learning of new knowledge Automated construction of newrules and Markov decision trees Tools for manual modificationof the available knowledge Distribution model: Executable code and documentation System requirements: High-end Windows desktop computer

  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

  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

  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.

  9. RAPID: Availability All versions of RAPID will demonstrate all capabilities – with increasing functionality over time, with primary emphasis on:

  10. Questions?

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