Objective 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. Fields of science 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 Topic(s) ERC-2017-ADG - ERC Advanced Grant Call for proposal ERC-2017-ADG See other projects for this call Funding Scheme ERC-ADG - Advanced Grant Coordinator RHEINISCH-WESTFAELISCHE TECHNISCHE HOCHSCHULE AACHEN Net EU contribution € 2 491 250,00 Address Templergraben 55 52062 Aachen Germany See on map Region Nordrhein-Westfalen Köln Städteregion Aachen Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00 Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all RHEINISCH-WESTFAELISCHE TECHNISCHE HOCHSCHULE AACHEN Germany Net EU contribution € 2 491 250,00 Address Templergraben 55 52062 Aachen See on map Region Nordrhein-Westfalen Köln Städteregion Aachen Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
