Lecturer: Vaishak Belle

The tension between reasoning and learning remains fundamental to artificial intelligence. This course surveys the intersection of logic and learning, exploring how these historically distinct paradigms can be unified. We examine three strands: logic versus learning (including weighted model counting and knowledge compilation), machine learning for logic (inductive logic programming, Bayesian scoring, PAC-semantics), and logic for machine learning (probabilistic programming, algebraic model counting, abstraction). The course emphasizes both foundations and algorithmic techniques. Course participants will gain understanding of statistical relational learning, neuro-symbolic systems, and the mathematical ideas connecting symbolic reasoning with data-driven approaches. The material bridges classical AI and modern machine learning, preparing researchers for cross-over applications by unifying reasoning and learning.

After an introductory survey, we will focus on the idea of using symbolic structures for generating counterfactual explanations, as well as the use of LLMs to generate symbolic structures, which are then solved exactly to complement (and correct) LLM-based inference.