# Logic and Learning

**Nada Lavrac and Luc De Raedt**
##
Course Description

Since the very start of machine learning, logic has been very popular as
a representation language for inductive concept- learning and the
possibilities for learning in a first order representation have been
investigated. This is due to the fact that first order logic extends
propositional representations and therefore also the scope of machine
learning. In database terminology, propositional techniques learn from a
single relation in a relational database, whereas first order approaches
cope with multiple relations. Learning in first-order logic is therefore
of special interest to researchers and practitioners in machine
learning, data mining, knowledge discovery in databases, knowledge
representation and logic programming.
The tutorial will provide a complete survey of "Logic and Learning" and
will concentrate on learning in first order logic. Early research
involved structural matching, least general generalization, model
inference, and theory restructuring. Recently, the area of logic and
learning has concentrated on Inductive Logic Programming, which studies
inductive machine learning within the representation of logic
programming.

The course gives an overview of the history, techniques and applications
of logic and learning. Logical and algorithmic foundations of this field
are emphasized, and illustrated by means of well-known systems and
algorithms. This includes: learning as search, logical representations,
operators and settings for induction, state-of-the-art methods and
systems, and applications in knowledge discovery and logic
programming. For use in data mining and knowledge discovery, pointers to
the available public domain systems are provided and discussed.

The course assumes basic knowledge of artificial intelligence and logic
(e.g. some notions about Prolog and/or the predicate calculus).
##
About the Lecturers

**Nada Lavrac**
is a research associate at the J. Stefan Institute, Ljubljana
(Slovenia). Her main research interest is in inductive logic programming
and medical applications of machine learning. She is co-author and
editor of several books published by Sigma Press, MIT Press, Kluwer and
Springer, including "Inductive Logic Programming: Techniques and
Applications" (Ellis Horwood 1994). She was a coordinator of ILPNET, the
European Scientific Network in Inductive Logic Programming (1993-96).
**Luc De Raedt**
is a post-doctoral researcher and a (part-time) assistant professor at
the Katholieke Universiteit Leuven (Belgium). His main interest is in
inductive logic programming. He is a coordinator of the European ESPRIT
III and IV projects on Inductive Logic Programming, was chair of the
ILP-95 workshop, and he has published two books on inductive logic
programming. He has given tutorials and invited talks on ILP at
ISMIS-93, ILPS-93, MSL-96, LOPSTR 96, etc.

higuchi@etl.go.jp
Last modified: Thu Feb 20 13:50:38 JST 1997