Logic-Based Learning

Module aims

In this module you will have the opportunity to:

  • acquire foundation knowledge and basic principles of logic-based machine learning
  • cover foundational semantics concepts and methods for logic-based machine learning
  • familiarise with state-of-the-art learning algorithms and heuristics
  • study state-of-the-art machine learning methods for Answer Set Programming (ASP)
  • develop skills for formalising logic-based learning tasks for solving real-world problems
  • explore probabilistic logic-based inference and learning

Learning outcomes

 Upon successful completion of this module you will be able to:

  • explain the semantic foundations and differences of logic-based machine learning problems
  • formalise a learning task as a logic-based learning task
  • apply algorithms to solve learning tasks in different semantic contexts
  • use Answer Set Programming to solve real-word problems
  • apply algorithms for learning Answer Set programs
  • evaluate current forms of probabilistic logic-based inference and learning
  • explain and apply state of the art algorithms for probabilistic learning

Module syllabus

  • Deductive, abductive and inductive reasoning
  • Bayesian reasoning techniques
  • Answer Set Programming
  • Top-down and bottom-up approaches to learning
  • Meta-level logic-based learning
  • Monotonic and non-monotonic learning
  • Enhanced logic programming systems (Progol, TopLog, Metagol, TAL, Imparo, ASPAL and ILASP)
  • Soundness and completeness of learning algorithms
  • Probabilistic/ stochastic inductive learning
  • Probabilistic logic programming 


 Recommended modules include Logic, and preferebly Declarative Programming (Prolog), but not required.

Teaching methods

The material will be taught through traditional lectures, backed up by unassessed, formative exercises designed to reinforce the material as it is taught. The lectures will introduce the theoretical foundations and algorithms for the various learning approaches and provide examples on how a learning problem can be formlaised and solved in each of the learning tasks.  You will also be able to access an ILP Framework platform where various ILP systems will be made available. Some of the tutorial hours will be run as hands-on sessions where you will solve the exercises in class using the ILP Framework.

The Piazza Q&A web service will be used as an open online discussion forum for the module.    


There will be two courseworks that collectively contribute 20% of the mark for the module. There will be a final written exam, which counts for the remaining 80% of the marks.

Feedback on the formative exercises will be given in class. The assessed courseworks will be accompanied by individual written feedback. Class-wide feedback on the assessed coursework will also be given.   

Reading list

Module leaders

Professor Alessandra Russo