In this talk I will present an Adaptive Logic: LATAr , which together with a contraction procedure RETRO and a way to distinguish kinds of premises (knowledge, hypotheses, observations), serves as a formal setting for hypotheses generation and testing in the empirical sciences.
As any other Adaptive Logic, LATAr has a dynamic proof theory, one that allows for a line in a proof to be deleted when it is found that it no longer observes the conditions under which it was obtained in the first place. In addition, our LATAr combines deductive and abductive steps in its dynamic proofs.
LATAr serves as a model for medical reasoning, more in particular, for the construction of diagnoses in neurology. As opposed to other abductive models, this one takes into account the fact that diagnostic hypotheses are either produced by an abductive rule or just by assertion, the latter aiming to capture the case when a medical doctor aims at refuting a hypothesis.
I will start by presenting some challenges for modeling the process of medical diagnosis via a real-case example to then introduce LATAr and finally present the case reviewed in the framework of this logic.
The research reported here was published in the Logic Journal of the IGPL 21(6): 915-913. 2013. doi:10.1093/jigpal/jzt005.