Contributions à l’élaboration de connaissances qualitatives en bio-informatique

This habilitation thesis gathers several works in the field of formal methods for biology. In our works, we tackled the challenge of modeling and analyzing the dynamics of large-scale biological regulatory networks. To address this issue, we identified some relevant class of formal models on which it is possible to perform effective analysis of its dynamics. After discussing the different modeling criteria to be taken into account in biology, we introduce the Process Hitting framework. We then present the methods that we designed to analyze such models, and their respective merits and limitats. Finally, we give an overview of recent research aiming to build a fruitful link between machine learning, logic programming, model-checking and bioinformatics. This allows us to bring out a new set of scientific questions.Cette synthèse pour l’HDR est un recueil de plusieurs travaux dans le domaine des méthodes formelles pour la biologie. Devant l’enjeu que représente la modélisation et l’analyse de la dynamique de réseaux de régulation biologiques à grande échelle, nous avons identifié une classe pertinente de modèles formels sur laquelle il est possible de mener des analyses efficaces de la dynamique. Après avoir discuté les différents critères de modélisation à prendre en compte en biologie, nous introduisons ainsi le formalisme des Frappes de Processus. Nous présentons ensuite les méthodes d’analyse conçues pour ce paradigme, leurs mérites et leurs limites. Enfin, nous revenons sur des résultats plus récents, consécutifs à l’établissement de liens fructueux entre l’apprentissage automatique, la programmation logique, le model-checking et la bio-informatique, ce qui nous permet de faire émerger un ensemble de nouvelles questions scientifiques

Inductive logic programming at 30

Inductive logic programming (ILP) is a form of logic-based machine learning. The goal of ILP is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we survey recent work in the field. In this survey, we focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs that generalise from few examples, (iii) new approaches for predicate invention, and (iv) the use of different technologies, notably answer set programming and neural networks. We conclude by discussing some of the current limitations of ILP and discuss directions for future research.Comment: Extension of IJCAI20 survey paper. arXiv admin note: substantial text overlap with arXiv:2002.11002, arXiv:2008.0791

Relational reinforcement learning for planning with exogenous effects

Probabilistic planners have improved recently to the point that they can solve difficult tasks with complex and expressive models. In contrast, learners cannot tackle yet the expressive models that planners do, which forces complex models to be mostly handcrafted. We propose a new learning approach that can learn relational probabilistic models with both action effects and exogenous effects. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. We also show how to combine this learner with reinforcement learning algorithms to solve complete problems. Finally, experimental validation is provided that shows improvements over previous work in both simulation and a robotic task. The robotic task involves a dynamic scenario with several agents where a manipulator robot has to clear the tableware on a table. We show that the exogenous effects learned by our approach allowed the robot to clear the table in a more efficient way.Peer ReviewedPostprint (published version