Artificial Intelligence (3 Years) [BSc]

Symbolic AI


Unit code: COMP24412
Credit Rating: 10
Unit level: Level 2
Teaching period(s): Semester 2
Offered by School of Computer Science
Available as a free choice unit?: Y

Requisites

None

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.

Aims

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.

Overview

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.

Teaching and learning methods

Lectures

22 in total, 2 per week

Laboratories

10 hours in total, 5 2-hour sessions.

Learning outcomes

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

Employability skills

  • Analytical skills
  • Problem solving

Assessment methods

  • Written exam - 80%
  • Practical skills assessment - 20%

Syllabus

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

Logic

Lectures 7--8

Theorem-Proving

Lectures 9-13

Natural language syntax.

Lectures 14 - 17

Natural language semantics.

Lectures 18--22

Catch-up and revision.

Recommended reading

COMP24412 reading list can be found on the School of Computer Science website for current students.

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
  • Independent study hours - 64 hours

Teaching staff

Allan Ramsay - Unit coordinator

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