Clustering-guided SMT(LRA) learning

  • Tim Meywerk (Speaker)
  • Marcel Walter (Speaker)
  • Große, D. (Speaker)
  • Rolf Drechsler (Speaker)

Activity: Talk or presentation › Contributed talk › science-to-science

Description

In the SMT(LRA) learning problem, the goal is to learn SMT(LRA) constraints from real-world data. To improve the scalability of SMT(LRA) learning, we present a novel approach called SHREC which uses hierarchical clustering to guide the search, thus reducing runtime. A designer can choose between higher quality (SHREC1 ) and lower runtime (SHREC2 ) according to their needs. Our experiments show a significant scalability improvement and only a negligible loss of accuracy compared to the current state-of-the-art.
Period17 Nov 2020
Event titleInternational Conference on integrated Formal Methods (iFM)
Event typeConference
LocationAustriaShow on map

Fields of science

  • 202017 Embedded systems
  • 202005 Computer architecture
  • 102005 Computer aided design (CAD)
  • 102 Computer Sciences
  • 102011 Formal languages

JKU Focus areas

  • Digital Transformation

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