In this paper we present a comparison of several inductive programming (IP) systems. IP addresses the problem of learning (recursive) programs from incomplete specifications, such as input/output examples. First, we introduce conditional higher-order term rewriting as a common framework for inductive program synthesis. Then we characterise the ILP system GOLEM and the inductive functional syste...

This chapter of a collective book aims at presenting cardinal extensions of the EU model, based on the Choquet integral, which allow to take into account observed behaviors as in Allais’ paradox under risk or Ellsberg’s paradox under uncertainty, where the expected utility model is violated. Under a key axiom, the comonotonic independence axiom,Schmeidler under uncertainty, and Quiggin and Yaar...

On the basis of the previous models of inductive and steric effects, ‘inductive’ electronegativity and molecular capacitance, a range of new ‘inductive’ QSAR descriptors has been derived. These molecular parameters are easily accessible from electronegativities and covalent radii of the constituent atoms and interatomic distances and can reflect a variety of aspects of intraand intermolecular i...

In this paper we propose a distributed approach to the inductive learning problem and present an implementation of the Distributed Learning System (DLS). Our method involves breaking up the data set into different sub-samples, using an inductive learning program (in our case PLS1) for each sample, and finally synthesizing the results given by each program into a final concept by using a genetic...