RICCIO, N. C. R.; http://lattes.cnpq.br/2232437340737784; RICCIO, Nicia Cristina Rocha.
Resumo:
The intention here is to develop a theory of machine learning (in particular, propositional
abductive reasoning) using the framework of D. Gabbay's Labelled Deductive Systems (LDS), in the
light of Gillies' perspective on the dichotomy 'deductive versus inductive logic' where controlled
inference serves as the bridging notion. From this point of view, abductive reasoning, a topic
that attracts much interest in A I and automated reasoning research, can be seen as a kind of
controlled deduction where the control component will be represented by the labels of the LDS
framework. This work investigates the possibility of treating both object and meta-level aspects
of an abductive problem side by side: the set of possible explanations to an observed fact is
generated deductively (the object level) and, after that , the most interesting explanation is chosen
considering the preference criterion stated by the user (the meta-level). This way, i t is possible
to give an abductive problem a more plausible solution. Moreover, since the preference criterion,
which depends on the meaning of the labels, may vary, this characterization of abduction can easily
be adapted to different problem domains.