MARTINS, A. S.; http://lattes.cnpq.br/7650381270311620; MARTINS, Agenor de Sousa.
Abstract:
Perceptiun provides agents with information about the word they inhabit. To perceive by using
similarities and analogies is one of the most advanced forms of perceiving. It constitutes one of the
most fundamental aspects of human cognition. Computationally, similarity is the comer-stone upon
which relies the entire case-based reasoning technology (CBR), as an intelligent computing paradigm.
In its origin and nature, the CBR technology is affected by similarity in ali over its operational
aspects.
The thesis here detailed has the general objective of exploring the CBR computational paradigm
regarding to the similarity point of view and its implication for these paradigm processes. The start
point, is the recognition of insufficiencies of current similarity approaches, in general, andparticularly,
in the CBR domain where the CBR processes are dominated by euclidean metrics and statistical
nearest-neighbours techniques. In this thesis, we propose to enhance the CBR methodologies
by developing the application of Tversky-Gati cogniíive íheory to the CBR methodologies of case
indexing, case evaluation, case similarity itself, cascranking, and case-based query-answering. We
experiment with this computational extent of the cognitive theory in the empirical domain oíloanunderwriting
where a credit underwriter has to decide on recommending or rejecting credit applications
based on their attribute similarities.