Department of Computer Science, Erasmus University of Rotterdam Inductive Logic Programming (ILP) is the intersection of Logic
Programming and Machine Learning. Most ILP researchers are rather
oriented towards implementation and application. However, together
with Ronald de Wolf Irecently published a book which gives a unified
and rigorous treatment of the theoretical foundations of ILP (S-H.
Nienhuys-Cheng & R. de Wolf, 'Foundations of Inductive Logic Programming'
Springer-Verlag, LNAI-tutorial 1228, May 1997). This course will contain the essentials of this book, focussing,
however, on examples rather than complete proofs of results. Our
book has two parts, one on logic and logic programming, the second
on ILP. I intend to treat the first part fairly quickly in the
first two sessions (in particular if someone else already gives
a course on logic or logic programming), devoting the last three
sessions to various ILP techniques and their properties. Session 1: Syntax and semantics of first order logic; interpretations
and models; normal forms; Herbrand models. Session 2: Substitution and unification; resolution-based theorem
proving; subsumption theorem and refutation completeness; SLD-resolution.
Session 3: What is Inductive Logic Programming; Shapiro's framework
for model inference; how to find a false clause by backtracing;
how to find a correct program by unfolding. Session 4: The lattice structure of the sets of atoms and clauses
under the generality order of subsumption (particularly least
generalizations); existence and nonexistence of covers in various
search spaces and generality orders. Session 5: Completeness and properness of refinement operators;
how to search for a correct theory by means of refinement operators.

**Logic and Computation**
**F**OUNDATIONS OF** I**NDUCTIVE **L**OGIC **P**ROGRAMMING
**Introductory course**
**Second week**
Cheng@cs.few.eur.nl
**Course description**
**Prerequisites**
None
**Literature**
No specific recommendation