01. – 06. September 2019, Dagstuhl-Seminar 19361

Logic and Learning


Michael Benedikt (University of Oxford, GB)
Kristian Kersting (TU Darmstadt, DE)
Phokion G. Kolaitis (University of California – Santa Cruz & IBM Almaden Research Center – San Jose, US)
Daniel Neider (MPI-SWS – Kaiserslautern, DE)

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Logic and learning are central to Computer Science, and in particular to AI-related research. Already Alan Turing envisioned in his 1950 "Computing Machinery and Intelligence" paper a combination of statistical (ab initio) machine learning and an "unemotional" symbolic language such as logic. Currently, however, research in logic and research in learning interact far too little with each other; in fact, they are often perceived as being completely distinct or even opposing approaches. While there has been interest in using machine learning methods within many application areas of logic, the investigation of these interactions has usually been carried out within the confines of a single problem area. We believe that an interaction involving a broader perspective is needed. It would be fruitful to look for common techniques in applying learning to logic-related tasks, which requires looking across a wide spectrum of applications. It is also important to consider the ways that logic and learning, deduction and induction, can work together.

The main aim of this Dagstuhl Seminar is to bring together researchers from various communities related to logic and learning, and to create bridges between the two fields via the exchange of ideas ranging from the injection of declarative methods in machine learning to uses and applications of learning in logical contexts. This will include creating an understanding of the work in different applications, as well as an increased understanding of the formal connections between these applications and the development of a more unified view of the current attempts to synthesize deductive and inductive approaches. The seminar will explore the following three distinct strands of interaction between logic and learning.

  1. Machine Learning for Logic, including the learning of logical artifacts, such as formulas, logic programs, database queries and integrity constraints, as well as the application of learning to tune deductive systems.
  2. Logic for Machine Learning, including the role of logics in delineating the boundary between tractable and intractable learning problems, the construction of formalisms that allow learning systems to take advantage of specified logical rules, and the use of logic as a declarative framework for expressing machine-learning constructs.
  3. Logic vs. Machine Learning, including the study of problems that can be solved using either logic-based techniques or via machine learning, the exploration of the trade-offs between adopting logic-based methods vs. adopting learning-based methods in cases where both methods apply, and the development of benchmarks for comparing these methods.

Motivation text license
  Creative Commons BY 3.0 DE
  Michael Benedikt, Kristian Kersting, Phokion G. Kolaitis, and Daniel Neider


  • Artificial Intelligence / Robotics
  • Data Bases / Information Retrieval
  • Verification / Logic


  • Machine learning
  • Logic
  • Databases
  • Verification
  • Computational complexity


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