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MEng Computer Science with Industrial Experience / Course details

Year of entry: 2021

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Course unit details:
Knowledge Based AI

Unit code	COMP24412
Credit rating	10
Unit level	Level 2
Teaching period(s)	Semester 2
Offered by	Department of Computer Science
Available as a free choice unit?	Yes

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.

Pre/co-requisites

Unit title	Unit code	Requirement type	Description
Introduction to AI	COMP24011	Co-Requisite	Compulsory

Students who are not from the School of Computer Science must have permission from both Computer Science and their home School to enrol.

COMP24011 is a co-requisite of this course

Aims

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

Learning outcomes

ILO 1 Describe, apply and demonstrate syntactic and semantic formal models for natural language processing
ILO 2 Describe, differentiate and apply different knowledge representation formalisms for modelling knowledge bases
ILO 3 Explain how these KR formalisms affect the reasoning process
ILO 4 Apply and demonstrate knowledge-based learning methods
ILO 5 Write Prolog programs to solve automated reasoning tasks and explain how they will execute
ILO 6 Describe the syntax and semantics of first-order logic and use it to model problems
ILO 7 Apply reasoning techniques (transformation to clausal form, resolution, saturation) to establish properties of first-order problems
ILO 8 Explain the theoretical limitations of first-order logic and the associated reasoning methods

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 and learning methods

Lectures

22 in total, 2 per week

Laboratories

10 hours in total, 5 2-hour sessions.

Employability skills

Analytical skills
Problem solving

Assessment methods

Method	Weight
Written exam	30%
Written assignment (inc essay)	5%
Practical skills assessment	65%

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

Scheduled activity hours
Assessment written exam	2
Lectures	24
Practical classes & workshops	10

Independent study hours
Independent study	64

Teaching staff

Staff member	Role
Giles Reger	Unit coordinator

Additional notes

Course unit materials

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

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