Case-Based Reasoning is a fairly new Artificial Intelligence technique which makes use of past
experience as the basis for solving new problems. Typically, a case-based reasoning system stores
actual past problems and solutions in memory as cases. Due to its ability to reason from actual
experience and to save solved problems and thus learn automatically, case-based reasoning has
been found to be applicable to domains for which techniques such as rule-based reasoning have
traditionally not been well-suited, such as experience-rich, unstructured domains. This
applicability has led to it becoming a viable new artificial intelligence topic from both a research
and application perspective.
This dissertation concentrates on researching and implementing indexing techniques for casebased
reasoning. Case representation is researched as a requirement for implementation of
indexing techniques, and pre-transportation decision making for hazardous waste handling is used
as the domain for applying and testing the techniques.
The field of case-based reasoning was covered in general. Case representation and indexing were
researched in detail. A single case representation scheme was designed and implemented. Five
indexing techniques were designed, implemented and tested. Their effectiveness is assessed in
relation to each other, to other reasoners and implications for their use as the basis for a case-based
reasoning intelligent decision support system for pre-transportation decision making for hazardous
waste handling are briefly assessed. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 1997.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ukzn/oai:http://researchspace.ukzn.ac.za:10413/5761 |
| Date | January 1997 |
| Creators | Wortmann, Karl Lyndon. |
| Contributors | Petkov, Don., Senior, Eric. |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | English |
| Type | Thesis |
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