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This is an archived syllabus from 2013-2014

COMP24412 Symbolic AI syllabus 2013-2014

COMP24412 Symbolic AI

Level 2
Credits: 10
Enrolled students: 53

Course leader: Allan Ramsay

Additional staff: view all staff

Assessment methods

  • 80% Written exam
  • 20% Practical skills assessment
Sem 2 Lecture 1.3 Mon 14:00 - 14:00 -
Sem 2 Lecture 1.3 Tue 16:00 - 16:00 -
Sem 2 A w3+ Lab LF31 Tue 11:00 - 11:00 G
Sem 2 A w3+ Lab LF31 Thu 11:00 - 11:00 I
Themes to which this unit belongs
  • Natural Language, Representation and Reasoning


Intelligent systems need to be able to represent the world, reason about it, and communicate about it. This course provides an introduction to the key ideas in automated reasoning and to natural language processing (i.e. to the ideas and techniques that are used in order for computers to use the languages, like English, that we use for communicating with other people). 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 explain basic techniques of AI programming, with special focus on the Prolog programming language and its application to processing natural language.


The following list specified the order in which material will be covered; however, it is not a timetable. Lectures may take more than one session if required. There is a block of time at the end of the course for catching up and revision.

Lectures 1--3

Basic Prolog programming

Lecture 4

Search techniques in AI

Lectures 5--6


Lectures 7--8


Lectures 9-13

Natural language syntax.

Lectures 14 - 17

Natural language semantics.

Lectures 18--22

Catch-up and revision.

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:

Learning outcomes are detailed on the COMP24412 course unit syllabus page on the School of Computer Science's website for current students.

Reading list

Mathematical Logic for Computer Science Ben-Ari, Mordechai.9781447141297Springer London ; Imprint Springer2012.
Learn Prolog now! Blackburn, Patrick, 1959-1904987176Collegec2006.
An introduction to description logic Baader, Franz,9781139025355Cambridge University Press2017.
Knowledge representation and reasoning Brachman, Ronald J.,9781558609327; 1558609326Morgan Kaufmann©2004.
Artificial intelligence : a modern approach Russell, Stuart J.1292024208; 9781292024202Pearson2014.
Artificial intelligence : a modern approach /Russell, Stuart J.9781292401171 (Proquest Ebook Central)Pearson,[2021]

Additional notes

Course unit materials

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