[PhD offer 2019] Reasoning with positive and negative cases

Supervisors: Emmanuel Nauer and Jean Lieber (firstname.lastname@loria.fr)

Keywords: case-based reasoning, positive and negative cases, knowledge representation and reasoning, symbolic machine learning

Thesis overview (see the pdf attached below for more details):

Case-based reasoning (CBR) consists in solving problems using
a case base, a case being the representation of a problem-solving
episode in a given domain. Classically, it is assumed that cases
from the case base are positive in the sense that they constitue
an acceptable solution for a user. The objective of this thesis
is to examine how CBR can evolve when taking into account both
positive and negative cases. An application will be developed
for validating this work either in the cooking domain,
the medical diagnosis domain and/or the machine translation domain.

en_thesis_Nauer_Lieber

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