Chapter: Artificial Intelligence(AI) - Expert System

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Knowledge Acquisition

1 Issues in Knowledge Acquisition 2. Techniques for Knowledge Acquisition

Knowledge Acquisition

 

 

Knowledge acquisition  includes  the  elicitation,  collection,  analysis,  modeling

 

and validation of knowledge.

 

1 Issues in Knowledge Acquisition

 

The important issues in knowledge acquisition are:

knowledge is in the head of experts

 

Experts have vast amounts of knowledge

 

Experts have a lot of tacit knowledge

 

They do not know all that they know and use

 

Tacit knowledge is hard (impossible) to describe

 

Experts are very busy and valuable people

 

One expert does not know everything

 

Knowledge has a "shelf life"


2. Techniques for Knowledge Acquisition

.

 

The techniques for acquiring, analyzing and modeling knowledge are : Protocol-generation techniques, Protocol analysis techniques, Hiera hy-generation techniques, Matrix-based techniques, Sorting techniques, Limited-information and constrained-processing tasks, Diagram-based techniques. Each of these are briefly stated in next few slides.

 

Protocol-generation techniques

Include many types of interviews (unstructured, semi-structured and structured), reporting and observational techniques.

Protocol analysis techniques

 

Used with transcripts of interviews or text-based information to identify basic knowledge objects within a protocol, such as goals, decisions, relationships and attributes. These act as a bridge between the use of protocol-based techniques and knowledge modeling techniques.

 

  Hiera hy-generation techniques

Involve creation, reviewing and modification of hiera hical knowledge.

Hiera hy-generation techniques, such as laddering, are used to build taxonomies or other hiera hical structures such as goal trees and decision networks. The Ladders are of various forms like concept ladder, attribute ladder, composition ladders.

 

Matrix-based techniques

 

Involve the construction and filling-in a 2-D matrix (grid, table), indicating such things, as may be, for example, between concepts and properties (attributes and values) or between problems and solutions or between tasks and resou es, etc. The elements within the matrix can contain: symbols (ticks, crosses, question marks ) , colors , numbers , text.

 

Sorting techniques

Used for capturing the way people compare and order concepts; it may reveal knowledge about classes, properties and priorities.

 

Limited-information and constrained-processing tasks

Techniques that either limit the time and/or information available to the expert when performing tasks. For example, a twenty-questions technique provides an efficient way of accessing the key information in a domain in a prioritized order.

 

Diagram-based techniques

Include generation and use of concept maps, state transition networks, event diagrams and process maps. These are particularly important in capturing the "what, how, when, who and why" of tasks and events.


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