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Whitaker– 5 minute madness Georgia Tech Research Institute PowerPoint Presentation
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Whitaker– 5 minute madness Georgia Tech Research Institute

Whitaker– 5 minute madness Georgia Tech Research Institute

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Whitaker– 5 minute madness Georgia Tech Research Institute

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  1. Whitaker– 5 minute madnessGeorgia Tech Research Institute Elizabeth T. Whitaker, Ph.D. Betty.Whitaker@GTRI.GATech.edu

  2. Case-Based Reasoning for Knowledge Discovery 2006 Present GTRI investigated analytic strategies used in the process of discovering new knowledge, as part of the ARDA/DTO Novel Intelligence from Massive Data (NIMD) program. We designed & prototyped a software tool for intelligence analysts that uses case-based reasoning and case-based planning to plan and execute complex interdependent Internet searches to aid analysts in discovering information relevant to a tasking. Our case-based reasoning approach represents best-practice analytic strategies as domain-specific search plans which are stored in a case library. The prototype matches an analyst’s current problem with the most similar problem in the case library and adapts the associated search plan to solve the current problem. Implicit Search Strategies Discovered Knowledge Tasking Transform Future Case-Based Reasoning for Knowledge Discovery Knowledge Discovery Plans Discovered Knowledge Analyst Analyst

  3. Case-Based Reasoning for Knowledge Discovery 2006 Case-Based Reasoning for Knowledge Discovery (CBR for KD) Capabilities Cases • Does the organization possess the technical capabilities? • Does the organization have access to the raw materials? • What manufacturing resources are available? • Who are the experts in this area? • Who have the experts collaborated with and what are their capabilities? • What publications and education exist in this area? Analyst Case Based Reasoning for Knowledge Discovery Knowledge Request Knowledge Discovery Process Models Retrieval Feedback Adaptation Analyst Model Discovered Knowledge Plan Execution Case Library Internet Knowledge Discovery Plans

  4. IRAD: Brain-Based Cognitive Architecture for Training 2011 Text Story Case Library Learning Objective Immersive Interaction Environment Dialog Manager Student Model Interaction History Scenario Generation World Model Sequence of Events Student Task Decomposition Scenario Adaptation Domain Expert Brain-Based Model of Learning Performance Evaluation Scenario Case Library Brain Scans Traditional Testing fMRI Analysis Reflection (Self-Improvement)

  5. IARPA ICArUS: Integrated Cognitive-Neuroscience Architectures for Understanding Sensemaking GTRI’s role • Provide machine learning and case-based reasoning expertise, input and guidance for development of ICArUS requirements and architecture. (Whitaker, Isbell, Hale) • Define the theoretical basis for models of the Parietal Cortex and Anterior Cingulate Cortex , suitable for forming the bases for development of computational models. (Eric Schumacher) • Support design and development of ICARUS systems to process challenge problem data

  6. DARPA Deep Green Conflict AnalysisSystem Dynamics models for COA analysis in support of a commander’s decision-making B1 R6 B6 B2 B7 B3 R5 B8 B4 R4 R2 R3 R1 B5 Conceptual Entanglement Discovery Translation into System Dynamics Entanglement could be caused by unplanned delays or changes in speed 2009

  7. DARPA Integrated Learning Project Data BB Goals BB Learning Human Expert Trace 2008 GTRI collaborated with a large team of researchers on the DARPA Integrated Learning project, which had as its goal to research the integration of multiple machine learning paradigms to learn to solve a problem by observing an expert in a single problem-solving session. GTRI, collaborating with the Georgia Tech College of Computing, developed a case-based learner & reasoner to perform as part of the integrated learning activity. Constraint Learner Hierarchical Case Learner Case Learner Constraints Constraints Hierarchical Case Base Case Base Performance Hierarchical Retrieval Proposed Solutions Performance Goals (PG) Adaptation PG PG Adapted Cases Retrieved Cases Adaptation Failure

  8. DARPA BLADE: Behavioral Learning for Adaptive Electronic Warfare Learning Orchestrator Training Trace • Learning and Reusing Cases for Classifying EW Signals and Generating Countermeasures • Using Case-based learning for to supply support for EW planners by learning cases from observation of the application of countermeasures • Integrating case-based reasoning and planning techniques with other reasoning components and approaches to provide more robust EW countermeasure synthesis through meta-reasoning • Development of an intelligent similarity metric for active case-learning of EW countermeasures BDA Constraint Learner Protocol Case Learner Countermeasure Case Learner Constraints Performance Constraints Protocol Case Base Countermeasure Case Base Two-Stage Retrieval Proposed Solutions CountermeasureSolutions Human Feedback Adaptation Target Problem Problem Adaptation Failure Retrieved Cases

  9. IARPA SIRIUS Project Domain Expert Module Serious Game Environment Content Selector Critic Module Learning Content Library CBR Adaptation Student Modeler Student Model Student Log Cognitive Biases Curriculum Model Learning Theory Teaching Theory

  10. DARPA Narrative Networks --PARABLE Architecture Case Library Operator Desired Influence Narrative Templates Narrative Composition Plan Library Narrative Components O O Subproblems Case Retrieval Algorithm with Similarity Metric Working Memory Narrative Composition Case Population Characteristics Plan Execution System Plan Instantiation with Adaptation Instantiated Plan Agent Agent Agent Narrative Template Draft Narrative Data, History, Strategies Introspection Agent Draft Narrative

  11. Questions and Discussion