Towards monotonous functions approximation from few data with Gradual Generalized Modus Ponens: application to materials science

Hiba Hajri (1) , Jean-Philippe Poli (1) , Laurence Boudet (1)

1 LI3A - Intelligence Artificielle et Apprentissage Automatique

In this paper, we present a new approach to predict monotonous functions based on approximate reasoning and in particular on the Gradual Generalized Modus Ponens (GGMP) in fuzzy logic. We propose to optimise the parameters of such fuzzy rules with a genetic algorithm considering few experimental data. We use our approach to predict some properties of materials from their manufacturing process parameters. We automatically extract causality, seek for graduality and then set up the GGMP. We tested on both toy and real world datasets. We also discuss the importance of gradual knowledge in materials science.

Keywords: Fuzzy logic; Gradual Generalized Modus Ponens; Knowledge Extraction; Materials Science; Monotonous Function Prediction; Model Interpretability; artificial intelligence; online learning; machine learning; Materials science and technology; Fuzzy sets; Manufacturing processes; Toy manufacturing industry; Predictive models

Domains: Artificial Intelligence [cs.AI]; Material chemistry

Hiba Hajri, Jean-Philippe Poli, Laurence Boudet. Towards monotonous functions approximation from few data with Gradual Generalized Modus Ponens: application to materials science. IEEE International Conference on Tools with Artificial Intelligence, Nov 2021, Washington (virtual conference), United States. pp.796-800, ⟨10.1109/ICTAI52525.2021.00127⟩. ⟨cea-03616377⟩