Objectif Probabilistic programs describe recipes on how to infer statistical conclusions about data from a complex mixture of uncertain data and real-world observations. They can represent probabilistic graphical models far beyond the capabilities of Bayesian networks and are expected to have a major impact on machine intelligence.Probabilistic programs are ubiquitous. They steer autonomous robots and self-driving cars, are key to describe security mechanisms, naturally code up randomised algorithms for solving NP-hard problems, and are rapidly encroaching AI. Probabilistic programming aims to make probabilistic modeling and machine learning accessible to the programmer.Probabilistic programs, though typically relatively small in size, are hard to grasp, let alone automatically checkable. Are they doing the right thing? What’s their precision? These questions are notoriously hard — even the most elementary question “does a program halt with probability one?” is “more undecidable” than the halting problem — and can (if at all) be answered with statistical evidence only. Bugs thus easily occur. Hard guarantees are called for. The objective of this project is to enable predictable probabilistic programming. We do so by developing formal verification techniques.Whereas program correctness is pivotal in computer science, the formal verification of probabilistic programs is in its infancy. The project aims to fill this barren landscape by developing program analysis techniques, leveraging model checking, deductive verification, and static analysis. Challenging problems such as checking program equivalence, loop-invariant and parameter synthesis, program repair, program robustness and exact inference using weakest precondition reasoning will be tackled. The techniques will be evaluated in the context of probabilistic graphical models, randomised algorithms, and autonomous robots.FRAPPANT will spearhead formally verifiable probabilistic programming. Champ scientifique engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehiclessocial sciencessociologyindustrial relationsautomationengineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringroboticsautonomous robotsnatural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learningnatural sciencesmathematicsapplied mathematicsstatistics and probability Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Thème(s) ERC-2017-ADG - ERC Advanced Grant Appel à propositions ERC-2017-ADG Voir d’autres projets de cet appel Régime de financement ERC-ADG - Advanced Grant Institution d’accueil RHEINISCH-WESTFAELISCHE TECHNISCHE HOCHSCHULE AACHEN Contribution nette de l'UE € 2 491 250,00 Adresse TEMPLERGRABEN 55 52062 Aachen Allemagne Voir sur la carte Région Nordrhein-Westfalen Köln Städteregion Aachen Type d’activité Higher or Secondary Education Establishments Liens Contacter l’organisation Opens in new window Site web Opens in new window Participation aux programmes de R&I de l'UE Opens in new window Réseau de collaboration HORIZON Opens in new window Coût total € 2 491 250,00 Bénéficiaires (1) Trier par ordre alphabétique Trier par contribution nette de l'UE Tout développer Tout réduire RHEINISCH-WESTFAELISCHE TECHNISCHE HOCHSCHULE AACHEN Allemagne Contribution nette de l'UE € 2 491 250,00 Adresse TEMPLERGRABEN 55 52062 Aachen Voir sur la carte Région Nordrhein-Westfalen Köln Städteregion Aachen Type d’activité Higher or Secondary Education Establishments Liens Contacter l’organisation Opens in new window Site web Opens in new window Participation aux programmes de R&I de l'UE Opens in new window Réseau de collaboration HORIZON Opens in new window Coût total € 2 491 250,00
