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COMP24412 Symbolic AI syllabus 2017-2018

COMP24412 materials

COMP24412 Symbolic AI

Level 2
Credits: 10
Enrolled students: 86

Course leader: Allan Ramsay


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

  • 80% Written exam
  • 20% Practical skills assessment
Timetable
SemesterEventLocationDayTimeGroup
Sem 2 Lecture 2.19 Tue 09:00 - 11:00 -
Sem 2 A w3+ Lab LF31 Thu 11:00 - 13:00 F
Sem 2 A w3+ Lab LF31 Tue 11:00 - 13:00 G
Themes to which this unit belongs
  • 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

Programme outcomeUnit learning outcomesAssessment
B1 C5 D2 D6Have a working knowledge of the Prolog programming language.
  • Lab assessment
A1 A5Be able to write programs for computing the meanings of a range of natural language sentences.
  • Lab assessment
A5Understand the operation and use of automated theorem-provers, and the theoretical reasons for their limitations.
  • Examination
  • Lab assessment
A1 A5Understand the fundamentals of natural language syntax.
  • Examination
  • Lab assessment
C5 D2Understand how meaning-representations for natural language sentences can be computed.
  • Lab assessment

Reading list

TitleAuthorISBNPublisherYearCore
Learn Prolog now!Blackburn, Patrick and Johan Bos and Kristina Striegnitz1904987176College Publications (Texts in Computing 7)2006
Representation and inference for natural language: a first course in computational semanticsBlackburn, Patrick and Johan Bos1575864967CSLI (Center for the Study of Language and Information)2005

Additional notes

Course unit materials

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