February 3 – 6 , 2019, Dagstuhl Seminar 19062

Bringing CP, SAT and SMT together: Next Challenges in Constraint Solving


Sébastien Bardin (CEA LIST, FR)
Nikolaj S. Bjorner (Microsoft Research – Redmond, US)
Cristian Cadar (Imperial College London, GB)
Vijay Ganesh (University of Waterloo, CA)

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List of Participants
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Constraint solving is at the heart of several key technologies, including program analysis, testing, formal methods, compilers, security analysis, optimization, and AI. During the last two decades, constraint solving has been highly successful and transformative: on the one hand, SAT/SMT solvers have seen a significant performance improvement with a concomitant impact on software engineering, formal methods and security; on the other hand, CP solvers have also seen a dramatic performance improvement, with deep impact in AI and optimization.

These successes bring new applications and new challenges, not yet met by current technologies, e.g.:

  • some fundamental constraints still lack efficient reasoning (e.g., floating-point arithmetic);
  • quantifiers are rarely taken into account - especially with model generation;
  • current approaches focus essentially on satisfiability and/or validity, while some applications would benefit from queries such as optimization or model counting.

While each of the SAT, SMT and CP communities has made progress on some aspects of these problems, no approach is able to tackle them all, and their combination is already a challenge. Moreover, while historically strongly connected, the SAT/SMT community and the CP community have had minimal interactions over the recent years.

The aim of this seminar is to reunify the Constraint Solving landscape and contribute at shaping it for the next decade, through identifying the next big challenges together with promising approaches. The seminar will bring together researchers from SAT, SMT and CP along with applications researchers in order to foster cross-fertilization of ideas, deepen interactions and identify the best ways to serve the application fields.

Especially, we expect strong interactions during the seminar around the following points:

  • [CP] advance propagation, domain-dedicated reasoning and (deep) constraint combination;
  • [SAT] branching heuristics, look-ahead, and conflict-clause learning methods;
  • [SMT] theory solvers and well-defined solver combinations;
  • How can we better serve the needs of applications researchers?
  • Finally, an important question is how to leverage machine learning in these contexts. The experience of the SAT community may bring here some answers.

  Creative Commons BY 3.0 DE
  Sébastien Bardin, Nikolaj S. Bjorner, Cristian Cadar, and Vijay Ganesh

Related Dagstuhl Seminar


  • Security / Cryptology
  • Software Engineering
  • Verification / Logic


  • Automated Decision Procedures
  • Constraint Programming

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