BSc Computer Science and Mathematics

Year of entry: 2024

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Course unit details:
Knowledge Based AI

Course unit fact file
Unit code	COMP24412
Credit rating	10
Unit level	Level 2
Teaching period(s)	Semester 2
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, differentiate and apply different knowledge representation formalisms for modelling knowledge bases
ILO 2 Describe the syntax and semantics of first-order logic (and the Datalog and Prolog fragments) and use it to model problems
ILO 3 Demonstrate the forward and backward chaining reasoning methods and compare their implementation and practical characteristics (e.g. efficiency, termination)
ILO 4 Apply resolution-based reasoning techniques (transformation to clausal form, resolution, saturation) to establish properties of first-order problems
ILO 5 Explain the theoretical limitations of reasoning techniques for (fragments and extensions of) first-order logic
ILO 6 Write Prolog programs to solve automated reasoning tasks and explain how they will execute
ILO 7 Differentiate between deductive, inductive and abductive reasoning and apply them to perform learning and inference in knowledge based systems
ILO 8 Relate knowledge based approaches to real world applications such as (but not limited to) program synthesis or circuit design verification

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

Inductive and Abductive Reasoning

Learning as a Logic Problem

Learning with Knowledge

Inductive Logic Programming

Explanations and Diagnosis

Explanation-Based Learning

Teaching and learning methods

Synchronous Sessions

11, 1 x per week

Lecture Video material

11 hr

Laboratories

10 hours in total, 5 2-hour sessions.

Employability skills

Analytical skills
Problem solving

Assessment methods

Method	Weight
Written exam	30%
Practical skills assessment	70%

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	22
Practical classes & workshops	10

Independent study hours
Independent study	77

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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