https://www.dagstuhl.de/22291

July 17 – 22 , 2022, Dagstuhl Seminar 22291

Machine Learning and Logical Reasoning: The New Frontier

Organizers

Sébastien Bardin (CEA LIST, FR)
Vijay Ganesh (University of Waterloo, CA)
Somesh Jha (University of Wisconsin-Madison, US)
Joao Marques-Silva (CNRS – Toulouse, FR)

For support, please contact

Jutka Gasiorowski for administrative matters

Michael Gerke for scientific matters

Motivation

Machine learning (ML) and logical reasoning have been the two key pillars of AI since its inception, and yet, there has been little interaction between these two sub-fields over the years. At the same time, each of them has been very influential in their own way. ML has revolutionized many sub-fields of AI including image recognition, language translation, and game playing, to name just a few. Independently, the field of logical reasoning (e.g., SAT/SMT/CP/first-order solvers and knowledge representation) has been equally impactful in many contexts in software engineering, verification, security, AI, and mathematics. Despite this progress, there are new problems, as well as opportunities, on the horizon that seem solvable only via a combination of ML and logic.

One such problem that requires one to consider combinations of logic and ML is the question of reliability, robustness, and security of ML models. For example, in recent years, many adversarial attacks against ML models have been developed, demonstrating their extraordinary brittleness. How can we leverage logic-based methods to analyze such ML systems with the aim of ensuring their reliability and security? What kind of logical language do we use to specify properties of ML models? How can we ensure that ML models are explainable and interpretable?

In the reverse direction, ML methods have already been successfully applied to making solvers more efficient. In particular, solvers can be modeled as complex combinations of proof systems and ML optimization methods, wherein ML-based heuristics are used to optimally select and sequence proof rules. How can we further deepen this connection between solvers and ML? Can we develop tools that automatically construct proofs for higher math?

To answer these and related questions we are organizing this Dagstuhl Seminar, with the aim of bringing together the many world-leading scientists who are conducting pioneering research at the intersection of logical reasoning and ML, enabling development of novel solutions to problems deemed impossible otherwise.

Motivation text license
  Creative Commons BY 4.0
  Sébastien Bardin, Vijay Ganesh, Somesh Jha, and Joao Marques-Silva

Dagstuhl Seminar Series

Classification

  • Logic In Computer Science
  • Machine Learning
  • Software Engineering

Keywords

  • SAT/SMT/CP solvers and theorem provers
  • Testing
  • Analysis
  • Verification
  • And Security of Machine Learning
  • Neuro-symbolic AI

Documentation

In the series Dagstuhl Reports each Dagstuhl Seminar and Dagstuhl Perspectives Workshop is documented. The seminar organizers, in cooperation with the collector, prepare a report that includes contributions from the participants' talks together with a summary of the seminar.

 

Download overview leaflet (PDF).

Publications

Furthermore, a comprehensive peer-reviewed collection of research papers can be published in the series Dagstuhl Follow-Ups.

Dagstuhl's Impact

Please inform us when a publication was published as a result from your seminar. These publications are listed in the category Dagstuhl's Impact and are presented on a special shelf on the ground floor of the library.