The Seminar is focused on Logic Programming (LP) which is usually called the language of Artificial Intelligence. The first part is focused on deduction in LP. The basic concepts as SLD-resolution, subsumption, deduction, refutation and unification are discussed. A soft introduction to Prolog language is given. Relationship between LP and SQL are introduced. Extensions of LP to handle uncertainty are presented. The second part is focused on induction in Logic Programming, called often Inductive Logic Programming (ILP) where two settings, namely the learning from entailment and learning from interpretations are distinguished. The model of ILP forms the basis for multi-relational data mining. Several techniques, as refinement graphs, inverse resolution, relative least generalization and inverse entailment are discussed. Extensions of ILP to handle uncertianty are introduced. Several applications of presented models are discussed during the seminar.

Recommended Reading:

1. J. W. Lloyd: Foundations of Logic Programming. Springer, Berlin, 1987.
2. Shan-Hwei Nienhuys-Cheng, Ronald de Wolf: Foundations of Inductive Logic Programming, 1997.
3. Nada Lavrac, and Sašo Džeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, New York, 1994.