Public Use Cases in Industry & R&D
Our Use Cases present the results of XTRACTIS modeling and include complete Benchmarks against Neural Networks, Boosted Trees, Random Forests, and Logistic Regression.
These studies illustrate the ability of XTRACTIS to automatically induce knowledge in the form of predictive and intelligible mathematical relationships from real-world data (public data or authorized private data).
For each application, we show how the induced décision system uses its fuzzy rules to compute explained predictions for new situations, i.e. unknown to the learning data set.
XTRACTIS robots explore a 27-dimension decision space of possible supply chain configurations to find the one that maximizes at best Operating Income given requests on local sales objectives, local production constraints, and ecological requirements. Objectives and constraints are either fuzzy (flexible) or binary (rigid).
Design an AI-based decision system that efficiently diagnoses driving situations from a few vehicle parameters and traffic light color, to select instantly and rationally the appropriate longitudinal action.
Automatic exploration of the 86-dimension continuous decision space of possible molecules formed by the 6 models (the molecule’s efficiency and its toxicity for 5 animal species), to satisfy simultaneously the 6 objectives with the highest possible degree: a new molecular profile that is an herbicide having the lowest toxicity for all species.
Discovery of the behavior of concrete from its age, formulation and some of manufacturing characteristics to predict its compressive strength and thus the level of its degradation, thanks to an explainable and accurate automated diagnosis.
Design an AI-based decision system that accurately predicts the upcoming risk of underwater pipes rupture considering the apparent complexity of the phenomenon, to plan rational maintenance operations.
Design an AI-based decision system that accurately predicts the functional degradation of a naval propulsion unit compressor, given the hyper-complexity of the phenomenon (strongly nonlinear behavior) in order to rationally plan explainable maintenance operations.
Discovery of effective emergency braking strategies from different driving situations to design an EBA that assists the driver by triggering automatically the ABS, while being able to explain each decision offline.
