Knowledge Acquisition

Knowledge Aquisition

Definition – The process of acquiring, organising, & studying knowledge.

Identified by many researchers and practitioners (in particular Feigenbaum) as the bottleneck in ES development.

Knowledge Aquisition

Two main types of sources of knowledge – documented (which can take many forms) and undocumented (usually in the expert’s mind).

Categories of Knowledge. (three main ones) Declarative – i.e. descriptive knowledge, facts. Procedural – how things are done, how to use

the declarative knowledge. Semantics – consider words & symbols & what

they mean, how they are related & manipulated. Reflects

cognitive structure.

Why is it difficult to transfer knowledge?

Hard to get experts to express how they solve problems

Representation on machine requires detailed expression i.e. at a very low level. Must be represented in a structured way.

Bringing together the ideas of all those involved in the knowledge transfer process.

Methods of knowledge aquisition

Interview techniques

Expert focused interview

Structured interview

‘Thinking aloud’ interview

To elicit general knowledge about

the domain

2nd phaseMore specific

questions from KE

Expert encouragedto talk while thinking

Helps understand expertsProblem solving

strategies

Other elicitation techniques Repertory grid (Kelly 1955)

Represents expert’s view of a problem Expert identifies important objects Expert identifies important attributes Expert establishes a bipolar scale with

distinguishable characteristics (traits) and their opposites.

Interviewer picks 3 objects & asks what distinguishes any 2 of these from the third.

Continues for several triplets of objects. Each object is given a score for each attribute that

represents a point on the range designated by the bipolar scale. (Usually use 1-3, or 1-5)

The way in which the objects are distinguished from each other becomes clear to KE. Used in some automated KA tools.

Observational techniques

Protocol analysis Like thinking aloud. Expert is observed carrying

out task and explains actions while doing it. Usually recorded.

Observation Saves experts time. Time consuming for KE.

Can be embarrassing for expert – might behave unnaturally.

Case studies Look at specific cases.

Automated KA – various approaches

Explanation facility can help, i.e. trials with knowledge coded so far.

Special knowledge base editors as interfaces to check for consistencies and completeness.

A KA aid known as TEIRESIAS was designed for work using EMYCIN.

Uses a NL interface & has expanded explanation facility. Translates each new rule to LISP and then back again so can show

inconsistencies, conflicts, etc.

KADS is a more general approach to automated KA.

Auto-intelligence – captures knowledge of expert through interactive interviews, distils knowledge, generates rule based system (see section rule induction)

Automated Rule Induction

Rules are generated by a computer system given a number of examples.

A series of examples (the training set) are provided and the inductive learning system generates rules from these.

These rules can then be used to assess further examples where the outcome is unknown.

This is done using algorithms.

A well used algorithm is Quinlan’s ID3 algorithm which generates a decision tree from the knowledge in the example cases, and then provides rules.

A later version of this algorithm is C5, and software to enable the use of it is See5 (windows version).

See5

See 5

Cross referencing in See5

Summary

Knowledge acquisition bottleneck

Approaches to KA Interview techniques Observational techniques Automated techniques Rule induction