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This is an archived syllabus from 2019-2020

COMP24412 Symbolic AI syllabus 2019-2020

COMP24412 Symbolic AI

Level 2
Credits: 10
Enrolled students: 101

Course leader: Giles Reger

Additional staff: view all staff

Additional requirements

  • Students who are not from the School of Computer Science must have permission from both Computer Science and their home School to enrol.

Assessment methods

  • 70% Written exam
  • 5% Coursework
  • 25% Practical skills assessment
Sem 2 Lecture Roscoe TH B Fri 14:00 - 15:00 -
Sem 2 Lecture Simon 3.44B Tue 15:00 - 16:00 -
Sem 2 A w3+ Lab Toot 1 Fri 12:00 - 14:00 G
Sem 2 A w3+ Lab 1.8 Tue 13:00 - 15:00 F
Themes to which this unit belongs
  • Natural Language, Representation and Reasoning


Intelligent systems need to be able to represent and reason about the world. This course provides an introduction to the key ideas in knowledge representation and different types of automated reasoning. The course is a mixture of theoretical and practical work: at the end of the course students will know the principles that such systems use, and they will have experience of implementing those principles in running systems.


The aim of this course is to provide the conceptual and practical (systems building) foundations for knowledge representation and reasoning in Artificial Intelligence.


First-Order Logic and Automated Reasoning
Syntax and Semantics
Translation to clausal form
Ordered Resolution
Saturation based proof search
Model Construction
Syntax and execution
Simple logical programs
Relation to backward chaining with Horn clauses
Theorem Proving with Prolog
Knowledge Representation
Ontological Engineering
Categories and Objects
Reasoning Systems for Categories
Semantic networks
Description logics
Reasoning with Default Information
Knowledge in Learning
A Logical Formulation of Learning
Inductive Logic Programming
Knowledge in Learning
Explanation-Based Learning
Learning Using Relevance Information
Natural Language Semantics
Interfacing with Natural Language Processing
Grammar & parsing
Montague Semantics
Semantic Parsing
Natural Logic Inference

Teaching methods


22 in total, 2 per week


10 hours in total, 5 2-hour sessions.

Feedback methods

The course has a number of lab exercises which are marked in the lab as usual, and feedback on these exercises is provided by written comments on the work and orally by the marker.

Study hours

  • Assessment written exam (2 hours)
  • Lectures (24 hours)
  • Practical classes & workshops (10 hours)

Employability skills

  • Analytical skills
  • Problem solving

Learning outcomes

On successful completion of this unit, a student will be able to:

  • Describe the syntax and semantics of first-order logic and use it to model problems
  • Apply reasoning techniques (transformation to clausal form, resolution, saturation) to establish properties of first-order problems 
  • Explain the theoretical limitations of automated theorem provers
  • Write Prolog programs to solve automated reasoning tasks and explain how they will execute
  • Describe, differentiate and apply different knowledge representation formalisms for modelling knowledge bases.
  • Explain how these formalisms affect the reasoning process.
  • Apply, demonstrate and program knowledge-based learning methods.
  • Apply, demonstrate and program formal models for natural language processing in the context of semantic parsing and natural logic inference.

Reading list

No reading list found for COMP24412.

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.