| Summary: | The thesis is a contribution to case-based reasoning (CBR). It rests on the observation that experts who express their knowledge in cases are inclined to state significant amounts of it only as informal arguments, which can be called “folk arguments”. These arguments are undervalued in CBR, sometimes not used at all. This work provides a means of capturing and representing such information in basically the natural form in which an expert states it, as an enhancement for traditional case-based knowledge. The aim is to exploit collections of facts and folk expert arguments in order to improve the quality of results obtained by CBR computations. In the novel CBR framework proposed, reasoning templates from knowledge engineering methodologies are offered as a systematic means of collecting and representing these arguments in cases – the “ArgCases framework”. Exploitation of procedures of numerical taxonomy in the investigation of case similarities and organisation of the case bases where facts and folk arguments are included then leads to a “Taxonomic ArgCases framework”. These contributions are validated in two applications where expert behaviour is primarily about reasoning on cases: allocation of frequencies for reliable reception of shortwave radio broadcasting, and the authentication (dating) of paintings. In both applications, case bases are constructed from information about expert analysis of problems, as captured from past records and with the help of new reasoning templates. Through proposal and inspection of taxonomies involving cases, it is shown how collections of factual and folk argumentation characteristics can be indexed in order to support the answering of alternative forms of query in CBR. The contributions of the thesis demonstrate how both numerical taxonomy and reasoning templates can be exploited within that area of artificial intelligence. In addition to these, its major contribution is to make a place for “folk arguments” within CBR.
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