High Performance Knowledge Bases, DARPA
Sponsoring Agency: AFOSR
Knowledge Acquisition Area


Constructing and Refining Knowledge Bases:
A Collaborative Apprenticeship Multistrategy Learning Approach


HPKB East Coast Meeting
Boston, April 1st, 1997


Gheorghe Tecuci
Learning Agents Laboratory
Computer Science Department
George Mason University, Fairfax, VA 22030

Slide Presentation

KB Development Architecture
KA Toolkit Architecture
Main Processes of Knowledge Acquisition
Illustrations of the KA Approach
Knowledge Representation: Semantic Network
Knowledge Representation: Plausible Version Space Rule
Acquiring Knowledge to Judge Relevance of Historical Sources
Rule Learning
Explanation of Input Example
Learning from Explanations
Generalizing the Explanation to an Analogy Criterion and Learning a PVS Rule
Solving Problems by Analogy
KB Refinement through Active Experimentation
Learning From a Positive Example
Generalization of the Plausible Lower Bound
Learning From a Negative Example
Specializations of the Two Plausible Bounds
Extending the Semantic Network as a Result of Rule Refinement
Acquiring Knowledge Representing the Expertise of a Company Commander
Semantic Network for Company Commander
Learning a Rule for Platoon Placement
Learned PVS Rule for Platoon Placement
KB Refinement
Specializations of Both Bounds of the Plausible Version Space
Correct Placement of Company-d Generated by the KA Agent
Updates to the Plausible Lower Bound
Refined PVS Rule
Some Research Issues for Rule Learning
Some Research Issues for KB Refinement

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Last Update: 4/23/97