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Published on: September 27, 2020
The implementation and evaluation of a theory for high level cognitive skill acquisition through expert systems
Richard J Koubek1, Gavriel Salvendy2
1a Department of Biomedical and Human Factors Engineering , Wright State University , Dayton , OH , 45435 , USA.
Expert systems modeling reveals that knowledge representation significantly impacts performance in computer program modification tasks. Super-expert (SE) systems use hierarchical, breadth-first knowledge, while expert (E) systems use narrow, depth-first knowledge.
Area of Science:
- Computer Science
- Cognitive Science
- Artificial Intelligence
Background:
- Previous research established performance differences between expert (E) and super-expert (SE) subjects in program modification.
- High-level controlled processes differ between E and SE individuals.
Purpose of the Study:
- To apply expert systems technology to model and understand performance differences between E and SE subjects.
- To test the hypothesis that knowledge representation is critical for SE performance.
- To explore practical applications of these findings.
Main Methods:
- Developed two prototype expert systems using E and SE knowledge representations.
- Qualitative analysis of system performance differences.
- Utilized expert systems technology for modeling.
Main Results:
- Significant performance disparities observed between the E and SE expert systems.
- Knowledge representation was identified as the key factor influencing performance differences.
- SE knowledge base exhibited a hierarchical structure with abstract categories (breadth-first approach).
- E knowledge base was narrow and task-specific (depth-first approach).
Conclusions:
- Knowledge representation is critical for super-expert performance in program modification.
- A hybrid expert system combining E and SE knowledge representations is recommended for optimal performance.
- Findings have theoretical implications for understanding expertise and practical applications in expert systems development.
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