Twenty-first International Joint Conference on Artificial Intelligence (IJCAI-09)

The Interdisciplinary Reach of Artificial Intelligence

Pasadena Conference Center

Sponsored by The International Joint Conferences on Artificial Intelligence (IJCAI) and The Association for the Advancement of Artificial Intelligence (AAAI)

IJCAI-09 Tutorial Program and Schedule - MA2
Monday, July 13, 8:30 am - 12:30 pm


Logical and Relational Learning
Luc De Raedt

The term “logical and relational learning” (LRL) refers to the subfield of artificial intelligence that lies at the intersection of knowledge representation and machine learning. It is concerned with learning in expressive logical or relational representations and is the union of inductive logic programming, (statistical) relational learning and multi-relational data mining. The topic of logical and relational learning has been around in the artificial intelligence communities for at least 40 years and is receiving a lot of attention today thanks to the popularity of statistical relational learning and the mining of structured data (including graphs, trees and sequences).

This tutorial is intended to provide a new synthesis of the field by summarizing some of the key lessons learned. These lessons will not only be concerned with the what, why and how of logical and relational learning, but will mainly show which principles of logical and relational learning are relevant to other disciplines in artificial intelligence. This includes machine learning and data mining techniques that do not use logical or relational representations (such as mining and learning from graphs, trees and sequences), knowledge representation formalisms that are not being learned yet, and various areas of artificial intelligence (such as natural language processing, human activity recognition and robotics) that can benefit from logical and relational learning techniques.

Luc De Raedt is a full research professor in Computer Science at the Katholieke Universiteit Leuven (Belgium). His research interests are in learning and mining in rich representations using logical, relational and probabilistic representations as well as their applications in artificial intelligence and bio- and chemo- informatics. He recently completed a book on Logical and Relational Learning with Springer.


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