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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

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Expertise in expert systems: knowledge acquisition for biological expert systems

M Edwards¹, R E Cooley

¹Computing Laboratory, University of Kent, Canterbury, UK.

Computer Applications in the Biosciences : CABIOS

|December 1, 1993

Summary

Expert systems need more than just facts; they require heuristic knowledge or

Area of Science:

Artificial Intelligence
Bioinformatics
Knowledge Engineering

Background:

Expert systems in biology often lack crucial heuristic knowledge.
Factual knowledge alone is insufficient for true expertise in a domain.
Heuristics, or 'rules of thumb', are essential for manipulating and interpreting factual data.

Purpose of the Study:

To highlight the importance of heuristic knowledge in expert systems.
To review knowledge acquisition techniques for eliciting heuristic knowledge.
To discuss the suitability of these techniques for small-scale biological expert systems.

Main Methods:

Review of existing biological expert systems.
Analysis of knowledge acquisition techniques.

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Focus on methods suitable for small-scale systems.

Emphasis on a modified 'twenty questions' technique for taxonomic knowledge.

Main Results:

Many current biological expert systems are limited by a lack of heuristic knowledge.
Several knowledge acquisition techniques are suitable for capturing heuristic knowledge.
A modified 'twenty questions' technique is effective for eliciting taxonomic knowledge.

Conclusions:

Heuristic knowledge is critical for developing expert systems that demonstrate true expertise.
Appropriate knowledge acquisition techniques are vital for capturing this heuristic knowledge.
The discussed methods are particularly relevant for biologists developing small-scale expert systems.