@phdthesis {PhD_Lieven, title = {Adaptieve Inductielogica{\textquoteright}s. Theory en Praktijk van het Inductief Redeneren}, year = {2005}, month = {November 30}, publisher = {Ghent University}, type = {phd}, author = {Haesaert, Lieven} } @article {haesaert2004non, title = {A non-probabilistic Approach to Inductive Prediction}, year = {2004}, abstract = {

The underlying idea behind the adaptive logics of inductive generalization is that most inductive reasoning can be explicated by simple qualitative means. Therefore, those classical models are selected that are ‘as uniform as possible ’ with respect to a certain set of (empirical) data. This led to the question if the same idea of uniformity can be applied if no generalizations are derivable. It is clear that in this case one may be still interested to make some direct inductive predictions. The main problem with this kind of prediction is that we lack a decision theory for it. In the present paper we make some proposals to deal with this problem. Our purpose here is to get more control over the difficult aspects of inductive prediction. In order to do so, we will not proceed in a probabilistic context, but we will apply the idea of minimizing the abnormalities in uniform models, an idea that derives from the adaptive logic programm. 1 Aim of this paper In our [1], we have presented some adaptive logics for induction based on Classical Logic (henceforth: CL). The underlying idea of these adaptive logics of induction is that most inductive reasoning does not proceed in terms of probabilities, and cannot be explicated in terms of probabilities, but can be explicated by rather simple qualitative means. In that paper we presented for example the adaptive logic for inductive generalization IL +m: from a set of data and (possibly falsified) background knowledge, inductive generalizations are ‘derived ’ 1. In the same paper we also

}, author = {Haesaert, Lieven} } @conference {152513, title = {Een adaptieve logica voor het beschrijven van inductie}, booktitle = {Handelingen van de 24ste {N}ederlands-{V}laamse Filosofiedag: Filosofie en Empirie}, year = {2002}, publisher = {Universiteit Amsterdam}, organization = {Universiteit Amsterdam}, author = {Haesaert, Lieven} } @article {DLn:induct2, title = {On Classical Adaptive Logics of Induction}, journal = {Logique et Analyse}, volume = {44}, number = {173-175}, year = {2001}, pages = {255{\textendash}290}, abstract = {

This paper concerns the inference of inductive generalizations and of predictions derived from them. It improves on the adaptive logic of induction from \emph{On a Logic of Induction} (Batens, Logic and Philosophy of Science, IV, 1, 2006, pp. 3-32) by presenting logics that are formulated strictly according to the usual adaptive standards. It moreover extends that paper with respect to background knowledge.

We present logics that handle inductive generalizations as well as logics that handle prioritized background knowledge of three kinds: background generalizations, pragmatic background generalizations (the instances of which may be invoked even after the generalizations are falsified), and background theories. All logics may be combined into a single system.

}, author = {Batens, Diderik and Haesaert, Lieven} }