On case representation and indexing in a case-based reasoning system for waste management.

Abstract

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.