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
| Semester | Event | Location | Day | Time | Group |
|---|---|---|---|---|---|
| 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 |
- Natural Language, Representation and Reasoning
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.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.
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.
Teaching methods
Lectures
22 in total, 2 per week
Laboratories
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
| Title | Author | ISBN | Publisher | Year |
|---|---|---|---|---|
| Mathematical Logic for Computer Science | Ben-Ari, Mordechai. | 9781447141297 | Springer London ; Imprint Springer | 2012. |
| Learn Prolog now! | Blackburn, Patrick, 1959- | 1904987176 | College | c2006. |
| An introduction to description logic | Baader, Franz, | 9781139025355 | Cambridge University Press | 2017. |
| Knowledge representation and reasoning | Brachman, Ronald J., | 9781558609327; 1558609326 | Morgan Kaufmann | ©2004. |
| Artificial intelligence : a modern approach | Russell, Stuart J. | 1292024208; 9781292024202 | Pearson | 2014. |
| 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.