Skip to navigation | Skip to main content | Skip to footer
Menu
Menu

This is an archived syllabus from 2018-2019

COMP24412 Symbolic AI syllabus 2018-2019

COMP24412 Symbolic AI

Level 2
Credits: 10
Enrolled students: 75

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
Timetable
SemesterEventLocationDayTimeGroup
Sem 2 Lecture Schuster BLACKETT TH Mon 11:00 - 12:00 -
Sem 2 w19 Lecture 1.1 Fri 09:00 - 10:00 -
Sem 2 w2+ Lecture 1.3 Fri 09:00 - 10:00 -
Sem 2 A w3+ Lab 1.8 Thu 09:00 - 11:00 F
Sem 2 A w3+ Lab 1.8 Tue 16:00 - 18:00 H
Themes to which this unit belongs
  • Natural Language, Representation and Reasoning

Overview

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.

Aims

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

Syllabus

First-Order Logic and Automated Reasoning
Syntax and Semantics
Translation to clausal form
Ordered Resolution
Saturation based proof search
Model Construction
 
Prolog
Syntax and execution
Simple logical programs
Relation to backward chaining with Horn clauses
Theorem Proving with Prolog
 
Knowledge Representation
Ontological Engineering
Categories and Objects
Events
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

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

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.