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Whitaker– 5 minute madness Georgia Tech Research Institute. Elizabeth T. Whitaker, Ph.D. [email protected]. Case-Based Reasoning for Knowledge Discovery. 2006. Present. - PowerPoint PPT Presentation
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Whitaker– 5 minute madnessGeorgia Tech Research Institute
Elizabeth T. Whitaker, [email protected]
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Case-Based Reasoning for Knowledge Discovery
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.
Future
Present
Implicit Search Strategies
Discovered Knowledge
Discovered Knowledge
Knowledge Discovery
Plans
Case-Based Reasoning for
Knowledge Discovery
Tasking
Analyst
Analyst
Transform
2006
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Case-Based Reasoning for Knowledge Discovery
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?
Case Based Reasoning for Knowledge Discovery
Knowledge Discovery Process Models
Analyst Model
Knowledge Discovery
Plans
Discovered Knowledge
Retrieval
Adaptation
KnowledgeRequest
Feedback
Internet
Analyst
Case Library
Plan Execution
2006
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IRAD: Brain-Based Cognitive Architecture for Training
Immersive Interaction Environment
Dialog Manager
Learning Objective
Student Model
World Model
ScenarioCase Library
Scenario Generation
Scenario Adaptation
Sequence of Events
Text StoryCase Library
Performance Evaluation
fMRI Analysis
Traditional Testing Brain Scans
Interaction History
Domain Expert
Reflection (Self-Improvement)
Student
Brain-Based Model of Learning
Task Decomposition
2011
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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
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DARPA Deep Green Conflict AnalysisSystem Dynamics models for COA analysis in support of a commander’s decision-making
PFG Input• Contains lightly-
processed scenario information
Extract Units and Locations• Paths of each unit
over time• Locations are
averaged across the 75 data rows in each sample
Determine Potential Conflicts• Iterate across all
path segments and find intersections in 2D
• Only uses location data, not time
Create System Dynamics Model• Model will be
graphically difficult to read but structurally accurate
Execute Model Over Various Input Ranges• Potential variables
to range: movement delays, speed, minor changes in latitude/longitude
Determine Actual Entanglements• Compare output
values to input ranges to determine sensitivity
• Show locations with a conflict intensity that exceeds a predetermined threshold
B1
B2
B3
B6
B5
B8
B7
R5
R1
R2 R4
R6
B4
R3
Translation into System DynamicsConceptual Entanglement Discovery
2009
Entanglement could be caused by unplanned delays or changes in speed
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DARPA Integrated Learning Project
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.
2008
Performance
DataBB
Constraint Learner
Hierarchical Case Learner Case Learner
Human Expert Trace
Constraints Hierarchical Case Base Case Base
Constraints
GoalsBB
Performance Goals (PG)
Hierarchical Retrieval
Adaptation
Retrieved Cases
PG
Adapted Cases
PG
Proposed Solutions
Adaptation Failure
Learning
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DARPA BLADE: Behavioral Learning for Adaptive Electronic Warfare
• 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•
Performance
Learning
Adaptation Failure
Orchestrator
Constraint Learner
Protocol Case Learner
Countermeasure Case Learner
Constraints ProtocolCase Base
Countermeasure Case Base
Target Problem
Two-Stage Retrieval
Adaptation
Retrieved Cases
Problem
Countermeasure Solutions
Training Trace
Constraints
Proposed Solutions
Human Feedback
BDA
Content SelectorDomain Expert Module
Student Model
SeriousGame
Environment
Student Modeler
Cognitive Biases Curriculum Model
Learning Content Library
CBR Adaptati
on
Teaching Theory
Learning Theory
Critic Module
Student Log
IARPA SIRIUS Project
Working Memory
Narrative Composition Case
Data, History, Strategies
Draft Narrative
Case Retrieval Algorithm
with Similarity Metric
Narrative Template
Instantiated PlanPlan Instantiationwith Adaptation
Plan Execution System
Agent Agent Agent
Population Characteristics
IntrospectionAgent
Operator
O O
Narrative Composition Plan
Library
CaseLibrary
Desired Influence Subproblems
Narrative Components
Narrative Templates
Draft Narrative
DARPA Narrative Networks --PARABLE Architecture
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Questions and Discussion