Techniques of machine learning have been successfully applied to various problems [1, 12].
Most of these applications rely on attribute-based learning, exemplified by the induction of …

An informal tutorial is presented for program synthesis, with an emphasis on deductive
methods. According to this approach, to construct a program meeting a given specification …

Given the recent successes of Deep Learning in AI there has been increased interest in the
role and need for explanations in machine learned theories. A distinct notion in this context …

The Prolog programmer's needs have always been the focus for guiding the development of
the SWI-Prolog system. This article accompanies an invited talk about how the SWI-Prolog …

We present positive PAC-learning results for the nonmonotonic inductive logic programming
setting. In particular, we show that first-order range-restricted clausal theories that consist of …

Simply logical | Guide books skip to main content ACM Digital Library home ACM home Google,
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Accurate identification and explication of program bugs requires an understanding of the
programmer's intentions. This paper describes a system called PROUST which performs …

The past few years have witnessed an significant interest in probabilistic logic learning, ie in
research lying at the intersection of probabilistic reasoning, logical representations, and …

Interactive theory revision | Guide books skip to main content ACM Digital Library home ACM
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Training set bugs are flaws in the data that adversely affect machine learning. The training
set is usually too large for manual inspection, but one may have the resources to verify a few …