1. Knowledge Acquisition

2. 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.

3. 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.

4. 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.

5. Methods of knowledge aquisition Interview techniques  Expert focused interview  Structured interview  ‘Thinking aloud’ interview To elicit general knowledge about the domain 2 nd phase More specific questions from KE Expert encouraged to talk while thinking Helps understand experts Problem solving strategies

6. 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.

7. 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.

8. 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)

9. 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).

10. See5

12. Cross referencing in See5

13. Summary Knowledge acquisition bottleneck Approaches to KA  Interview techniques  Observational techniques  Automated techniques  Rule induction