Laboratory of AI and Computer Science (LIACC)

University of Porto

Porto Portugal

Researchers Pavel Brazdil, Alípio Jorge, Luís Torgo, João Gama

ILP Areas Learning from algorithm sketches, Modular approaches to learning, Theory refinement, Multiple predicate learning, Learning in multi-agent environments, Learning from multiple sources, Evaluation of inductive methods.

Description of Research One of the main concerns of LIACC is how to turn ILP systems more practicable so that they could be of help when developing logic programs. In one line of work we have examined how we could adapt the ideas of structured programming to ILP [4]. Following the structured methodology, a complex task is split up into subtasks. Each subtask requires a specification of a particular predicate, or a group of predicates (= module), and generation of the required definition with the help of inductive methods. Our concerns are thus similar to those of in the area of multiple predicate learning (particularly intensional approaches).

In another line of work we have explored the possibility of giving the system some information about how the given problem could be solved. This information appears in the form of an algorithm sketch, which is an incomplete and somewhat vague representation of the computation, related to a particular example. In [1] we describe which sketches are admissible, give details of the learning algorithm. It employs a top-down covering method, but in addition exploits the information contained in the sketch. The implemented system is called SKIL1. We are planning to extend this system to multiple predicate (and modular) learning discussed earlier.

LIACC has been concerned with the problem that can arise in practice. Different subprograms may sometimes use a somewhat different predicate vocabulary. The composition of the final program requires then that all vocabulary differences get resolved. The method can itself emply induction, as is demonstrated in [6, 7]. The problem of different and/or incompatible vocabulary typically arise when subprograms get generated by different ILP systems (or agents). In this context the final program may consist of different parts, each generated by one particular system. In addition, we may need also certain interface theories which enable to relate the terms generated in different contexts.

Finally LIACC has also been concerned with evaluation of different learning algorithms. The aim is to obtain a system capable of examining a given dataset and giving a recommendation concerning the suitability of different learning methods. Although most of our previous effort was oriented towards evaluation of different classification algorithms [2], we are planning to extend this work to cater for ILP datasets/systems.

Recent Publications

[1] P. Brazdil, A. Jorge. Learning by refining algorithm sketches. Proc. European Conf. on Artificial Intelligence, ECAI-94, Amsterdam. J. Wiley, 1994.

[2] P. Brazdil, J. Gama, B. Henery. Characterizing the applicability of classification algorithms using meta-level learning. Proc. European Conf. on Machine Learning, ECML-94, Catania. Springer-Verlag, 1994.

[3] P. Brazdil, A. Jorge. Exploiting algorithm sketches in ILP. Proceedings of The Third International Workshop on Inductive Logic Programming, ILP’93, Jozef Stefan Institute, Ljubljana, Slovenia, 1993.

[4] P. Brazdil, A. Jorge. Modular approach to ILP: Learning from interactions between modules. Proceedings of the Workshop on Logical Approaches to Machine Learning, Vienna, Austria, 1992.

[5] P. Brazdil. Approaches to Inductive Logic Programming. In V. Marik, O. Stepankova, R. Trappl (eds.) Advanced Topics in AI, Lecture Notes in Artificial Intelligence, Vol. 617, 139–160, Springer-Verlag, 1992.

[6] P. Brazdil, S. Muggleton. Learning to relate terms in multiple agent environment. Proc. of European Working Session on Learning, EWSL-91, Lecture Notes in Computer Science, Vol. 482. Springer-Verlag, 1991.

[7] P. Brazdil. Learning in multi-agent environments. Proceedings of Second Workshop on Algorithmic Learning Theory. OHMSHA, 1991.

Contact Person Pavel Brazdil LIACC University of Porto Rua Campo Alegre, 823 4100 Porto Portugal Phone: +351 2 600 1672 Fax: +351 2 600 3654 E-mail: pbrazdil@ncc.up.pt