Universita‘ di Torino (TO) Dipartimento di informatica

Torino Italy

Researchers Francesco Bergadano, Daniele Gunetti, Gemma Cameirana, Umberto Trinchero, Stefano Brusotti, Nicola Olivetti.

ILP Areas ILP theory, Declarative bias, Theory revision.

Description of Research The research of the Torino’s ML group is concentrated on Declarative Bias, Theory revision and Theory. In the past few years, a lot of research was done on concept learning. More recently, attention has focused on the problem of declarative bias. A language based on clause sets has been developed, in order to specify in a simple and “logic programming oriented” way a bias for the ILP problem. This language has been used in the ILP learning system FILP [7], developed at the University of Torino. This system is able to learn logic programs from positive examples only, it can learn multiple predicates and does not need an extensionally complete set of examples of the target concept. Missing examples are queried to the user on the basis of those given initially. A similar approach has been also proposed for knowledge discovery in logic databases [1].

Another learning system, called TRACY, using clause sets as a language for specifying the bias, is under development at the University of Torino. TRACY is especially suitable for multiple predicate learning, theory revision and software engineering. It does not depend on the number and kind of given examples, and it is able to learn complete and consistent programs whenever they exist in the given hypothesis space. TRACY can start with an empty target theory or revise an existing one. The group in Torino is also working on the applications of ILP in software engineering, i.e. how using ILP techniques as an alternative approach to software development, testing, updating and reusing of logic programs [6, 2, 4]. The approach adopted in TRACY has also been used to show the difficulties of learning logic programs containing non declarative predicates [9].

More recent research is concerned with the formalization of the top-down approach to ILP in terms of inverse resolution as a deductive rule [8]. The research is showing that topdown methods can be seen as a special kind of theorem proving based on inverse resolution.

Recent Publications

[1] F. Bergadano. Inductive database relations. IEEE Trans. on Data and Knowledge Engineering 5 (6), 969–972, 1993.

[2] F. Bergadano. Test case generation by means of learning techniques. Proc. ACM SIGSOFT, Los Angeles, CA, 1993.

[3] F. Bergadano. The foundations of induction and machine learning. Minds and Machines 3, 31–51, 1993. Kluwer Academic Publishers.

[4] F. Bergadano, S. Brusotti, D. Gunetti, U. Trinchero. Inductive test case generation. Proc. Third Int. Workshop on Inductive Logic Programming, Bled, Slovenia, 1993.

[5] F. Bergadano, D. Gunetti. Functional inductive logic programming with queries to the user. In P. Brazdil (ed.) Proc. European Conference on Machine Learning, 323–328, Vienna, Austria, 1993. Springer-Verlag.

[6] F. Bergadano, D. Gunetti. Inductive synthesis of logic programs and inductive logic programming. Proc. Int. Workshop on Logic Program Synthesis and Transformation (LOPSTR-93), Leuven, Belgium, 1993. Springer-Verlag, Lecture Notes in Computer Science, Springer-Verlag.

[7] F. Bergadano, D. Gunetti. An interactive system to learn functional logic programs. Proc. 13th Int. Joint. Conf. on Artificial Intelligence, 1044–1049, Chambery, France, 1993. Morgan Kaufmann.

[8] F. Bergadano, D. Gunetti. Learning relations: Basing top-down methods on inverse resolution. In P. Torasso (ed.) Proc. Third Conf. of the Italian Association for Artificial Intelligence, 190–201, Torino, Italy, 1993. Springer-Verlag.

[9] F. Bergadano, D. Gunetti, U. Trinchero. The difficulties of learning logic programs with cut. Journal of Artificial Intelligence Research 1, 91–107, 1993. Morgan Kaufmann (to appear).

[10] A. Giordana, L. Saitta, F. Bergadano, F. Brancadori, D. Demarchi. ENIGMA: A system that learns diagnostic knowledge. IEEE Trans. on Knowledge and Data Engineering 5 (1), 15–28, 1993.

[11] L. Saitta, F. Bergadano. Pattern recognition and Valiant’s learning framework. IEEE Trans. on Pattern Analysis and Machine Intelligence, 15 (2), 145–154, 1993.

Contact Person Francesco Bergadano Dipartimento di Informatica Universita‘ di Torino Corso Svizzera 185 10149 - Torino Italy Phone: +39 11 7429216 Fax: +39 11 751603 E-mail: bergadan@di.unito.it