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What and Who
Title:Machine Learning Meets Formal Methods
Speaker:Daniel Neider
coming from:Max Planck Institute for Software Systems
Speakers Bio:
Event Type:Talk
Visibility:D1, D2, D3, D4, D5, RG1, SWS, MMCI
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Level:Public Audience
Date, Time and Location
Date:Wednesday, 13 June 2018
Duration:60 Minutes
Building:E1 5
Modern machine learning offers fascinating opportunities to simplify and automate complex logical tasks. Especially in the area of formal methods, machine learning has attracted great interest and led to innovative solutions to longstanding problems.

In this talk, I will make the case that formal methods and machine learning indeed complement each other extremely well. As a gentle introduction to this area of research, I will present two of my current student projects. The first project uses formal methods to prove properties of an artificial neural network obtained from
Caenorhabditis Elegans, a nematode (worm) whose brain and nervous system have been completely mapped. The second project applies machine learning techniques to formal methods with the goal of identifying algorithms based on how they modify the memory.

In the second part of this talk, I will present a novel machine learning setting, called ICE learning, which we have developed for learning correctness proofs of software. Moreover, I will illustrate how ICE learning can be used to automatically verify various types of programs and discuss its application to other infinite-state systems.

I will conclude my talk with an outline of future research directions.

Name(s):Connie Balzert
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Connie Balzert/MPI-INF, 06/08/2018 12:38 PM
Last modified:
Uwe Brahm/MPII/DE, 06/13/2018 07:01 AM
  • Connie Balzert, 06/08/2018 12:38 PM -- Created document.