Laboratoire de Recherche en Informatique (LRI)

Inference and Learning Group

Université Paris Sud Orsay France

Researchers Yves Kodratoff, Céline Rouveirol, Marta Franova, Claire Nédellec, Guillaume Leblanc.

ILP Areas Predicate invention, Declarative bias.

Description of Research The main research lines of LRI in the ESPRIT Basic Research Project 6020 “Inductive Logic Programming” are predicate invention and declarative bias.

The main focus in Predicate Invention is the study of the links and possible crossfertilization between ILP approaches to Predicate Invention and Program Synthesis methods that come from the Inductive Theorem Proving community. LRI’s system PRECOMAS [1, 2, 3, 4, 5] generates correct and computable definitions of predicates starting from specifications. It creates a new (recursive) predicate that characterizes the validity domain of the input specification. Other work in LRI in Predicate Invention and Program Synthesis is about the BMWk methodology, that discovers recursive relations among a set of terms. This methodology has been formalised in a term rewritting paradigm [12] and has been shown to exhibit some strong similarities and relevant improvements to related works in ILP.

In the field of Declarative Bias, LRI works on elicitation of learning biases both in knowledge intensive generalization [10] and in a subclass of empirical concept learners that follow a Generate and Test strategy. The most studied type of learning biases is the concept description language bias which statically restricts the set of learnable concepts. We focus in LRI on the study of search biases, (such as the adjustement of the generalisation/specialisation step), that directly influence learning performance such the number of examples needed for learning to converge, the learning speed, etc. [8, 9]. A prototype for a parameterisable learning system for the study of interaction between language and search biases is under developement.

Recent Publications

[1] M. Franova. A constructive proof for PRIME FACTORIZATION THEOREM: A result of putting it together in CONSTRUCTIVE MATCHING methodology. Rapport de Recherche No.780, L.R.I., Univ. de Paris-Sud, 1992.

[2] M. Franova, Y. Kodratoff. Predicate synthesis from formal specifications: Using mathematical induction for finding the preconditions of theorems. Rapport de Recherche No.781, L.R.I., Univ. de Paris-Sud, 1992.

[3] M. Franova, Y. Kodratoff. Simplifying implications in inductive theorem proving: Why and how? Rapport de Recherche No.788, L.R.I., Univ. de Paris-Sud, 1992.

[4] M. Franova, Y. Kodratoff, M. Gross.Constructive matching methodology: An extension and an application to a planning problem. Rapport de Recherche no. 838, L.R.I., Univ. de Paris-Sud, 1993.

[5] M. Franova, Y. Kodratoff, M. Gross. Constructive matching methodology: Formally creative or intelligent inductive theorem proving? In J. Komorowski, Z. W. Ras (eds.) Methodologies for Intelligent Systems - Proc. of ISMIS’93, Lecture Notes in Artificial Intelligence, Vol. 689, 476–485. Springer-Verlag, 1993.

[6] B. Duval. Abduction and induction for explanation based learning. Proc. Postconference Workshop on Abductive Reasoning of the International Conference on Logic Programming, Budapest, 1993.

[7] B. Duval. Relationships between machine learning and abductive reasoning. Proc. of European Working Session on Learning, EWSL-91, Lecture Notes in Computer Science, Vol. 482, 348–360. Springer-Verlag, 1991.

[8] C. Rouveirol, C. Nédellec. Biais pour les systemes incrementaux guides par les hypotheses. Actes des huitiemes Journees Francophones sur l’Apprentissage, 1993.

[9] C. Nédellec, C. Rouveirol. Hypothesis selection biases for incremental learning. Proceedings of the AAAI Spring Symposium Series workshop on Issues for Incremental Learning, 1993.

[10] C. Rouveirol, H. Ade, L. De Raedt. Bottom up Generalization in ILP. Proceedings of the IJCAI-93 workshop on ILP, 59–70, 1993.

[11] P. Flach. Predicate invention in inductive data engineering. Proc. European Conference on Machine Learning, Lecture Notes in Artificial Intelligence, Vol. 667. Springer-Verlag, 1993.

[12] G. Le Blanc. BMWk revisited – Generalization and Formalization of an algorithm for detecting recursive relations in term sequences. In F. Bergadano, L. De Raedt (eds.) Proc. Seventh European Conference on Machine Learning, ECML-94, Springer-Verlag, 1994.

Contact Person Céline Rouveirol Laboratoire de Recherche en Informatique (LRI) Inference and Learning Group Université Paris Sud Bât 490 F-91405 ORSAY Cédex France Email: celine@lri.fr Tel: +33 1 69416462 Fax: +33 1 69416586