Attribute-Value Learning Versus Inductive Logic
Programming: The Missing Links
(Extended Abstract)


Luc De Raedt

Department of Computer Science, Katholieke Universiteit Leuven
Celestijnenlaan 200A, B-3001 Heverlee, Belgium
email : Luc.DeRaedt@cs.kuleuven.ac.be


Abstract. Two contributions are sketched. A first contribution shows
that a special case of relational learning can be transformed into attribute-value 
learning. However, it is much more tractable to stick to the relational 
representation than to apply the sketched transformation. This
provides a sound theoretical justification for inductive logic programming. 
In a second contribution, we show how existing attribute-value
learning techniques and systems can be upgraded towards inductive logic
programming using the Leuven methodology and illustrate it using the
Claudien, Tilde, ICL, Warmr, TIC, MacCent and RRL systems.
References

1.	H. Blockeel and L. De Raedt. Top-down induction of first order logical decision
trees. Artificial Intelligence, 1998. To appear.
2.	H. Blockeel, L. De Raedt, and J. Ramon. Top-down induction of clustering trees.
In Proceedings of the 15th International Conference on Machine Learning, 1998.
3.	I. Bratko and S. Muggleton. Applications of inductive logic programming. Communications 
of the ACM, 38(11):6570, 1995.
4.	p. Clark and T. Niblett. The CN2 algorithm. Machine Learning, 3(4):261284,
1989.
5.	L. De Raedt, editor. Advances in Inductive Logic Programming, volume 32 of Frontiers 
in Artificial Intelligence and Applications. LOS Press, 1996.
6.	L. De Raedt. Logical settings for concept learning. Artificial Intelligence, 95:187
201, 1997.
7.	L. De Raedt and L. Dehaspe. Clausal discovery. Machine Learning, 26:99146,
1997.
8.	L. De Raedt and S. Dzeroski. First order jk-clausal theories are PAC-learnable.
Artificial Intelligence, 70:375392, 1994.
9.	L. De Raedt and W. Van Laer. Inductive constraint logic. In Proceedings of the
5th Workshop on Algorithmic Learning Theory, volume 997 of Lecture Notes in
Artificial Intelligence. Springer-Verlag, 1995.
10.	L. Dehaspe. Maximum entropy modeling with clausal constralnts. In Proceedings
of the 7th International Workshop on Inductive Logic Programming, volume 1297
of Lecture Notes in Artificial Intelligence, pages 109124. Springer-Verlag, 1997.
11.	L. Dehaspe and L. De Raedt. Mining association rules in multiple relations. In
Proceedings of the 7th International Workshop on Inductive Logic Programming,
volume 1297 of Lecture Notes in Artificial Intelligence, pages 125132. Springer-
Verlag, 1997.
12.	T. G. Dietterich, R. H. Lathrop, and T. Lozano-Pres. Solving the multiple-instance 
problem with axis-parallel rectangles. Artificial Intelligence, 89(1-2):31
71, 1997.
13.	S. Dzeroski and I. Bratko. Applications of inductive logic programming. In
L. De Raedt, editor, Advances in inductive logic programming, volume 32 of Fron-
tiers in Artificial Intelligence and Applications, pages 6581. IOS Press, 1996.
14.	S. Dzeroski, L. De Raedt, and H. Blockeel. Relational reinforcement learning. In
Proceedings of the International Conference on Machine Learning. Morgan Kaufmann, 1998.
15.	T. Mitchell. Machine Learning. McGraw-Hill, 1997.
16.	S. Muggleton and L. De Raedt. Inductive logic programming : Theory and methods. 
Journal of Logic Programming, 19,20:629679, 1994.
17.	J. Ross Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann series
in machine learning. Morgan Kaufmann, 1993.
18.	A. Srinivasan, S.H. Muggleton, M.J.E. Sternberg, and R.D. King. Theories for
mutagenicity: A study in first-order and feature-based induction. Artificial Intelligence, 
85, 1996.
19.	L. Valiant. A theory of the learnable. Communications of the ACM, 27:11341142,
1984.
