BSc Mathematics

Year of entry: 2024

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Course unit details:
Introduction to AI

Course unit fact file
Unit code	COMP24011
Credit rating	10
Unit level	Level 2
Teaching period(s)	Semester 1
Available as a free choice unit?	Yes

Overview

The Unit aims to make students familiar with the basic concepts and techniques of Artificial Intelligence. It provides the knowledge and understanding that underpins later course units in the subject taught in the Department.

Pre/co-requisites

Unit title	Unit code	Requirement type	Description
Mathematical Techniques for Computer Science	COMP11120	Pre-Requisite	Compulsory
Data Science	COMP13212	Pre-Requisite	Compulsory

COMP11120 (not a pre-requisite for CM)

Aims

A student completing this course should:

be able to implement basic search- and planning-algorithms from Artificial Intelligence, and apply them to real-world problems;
be able to apply first-order logic to model physical situations and reason about the effects of actions,
to appreciate the limitations of logic and to select appropriate responses to these limitations;
be able to develop formal ontologies to represent knowledge in different domains;
be able to select and apply the principal models of uncertainty employed in Artificial Intelligence in concrete problem-solving situations;
be able to solve the problem of sensor integration, and to implement simultaneous localization and mapping in robotics;
be able to apply techniques for representing (qualitative) temporal and spatial information in Artificial Intelligence;
have an appreciation of the central philosophical problems connected with artificial intelligence.

Learning outcomes

On the successful completion of the course, students will be able to:

ILO 1 To be able to implement basic search- and planning-algorithms from Artificial Intelligence, and apply them to real-world problems.
ILO 2 To be able to apply first-order logic to model physical situations and to reason about the effects of actions.
ILO 3 To appreciate the limitations of logic and to be able to select appropriate responses to these limitations.
ILO 4 To be able to develop formal ontologies to represent knowledge in different domains.
ILO 5 To be able to select and apply the principal models of uncertainty employed in Artificial Intelligence in concrete problem-solving situations.
ILO 6 To be able to solve the problem of sensor integration, and to implement simultaneous localization and mapping in robotics.
ILO 7 To be able to apply techniques for representing (qualitative) temporal and spatial information in Artificial Intelligence.

Syllabus

Topic 1. Search and planning:

problem-solving as search; adversarial games; classical planning.

Topic 2. Logic and reasoning

review of first-order logic; applications of logic to planning; logic versus reasoning; default `logic'.

Topic 3. AI and probability

review of probability theory; alternative representations of uncertainly; Bayes' networks.

Topic 4. Knowledge representation

ontology-driven database access; formal ontologies and knowledge-representation.

Topic 5. The periphery:

sensors and actuators; sensor integration, simultaneous localization and mapping.

Topic 6. Philosophical issues:

the Turing test; the meaning of `AI'; the problem of consciousness.

Teaching and learning methods

2 hours lectures per week (22 hours in total), 2 hours of lab per fortnight (8 hours in total)

Assessment methods

Method	Weight
Written exam	80%
Written assignment (inc essay)	20%

Feedback methods

Exam and assessments

Coursework:

Lab 1: Syllogisms

Lab 2: Games

Lab 3: Fuzzy logic

Lab 4: SLAM

Study hours

Scheduled activity hours
Lectures	22
Practical classes & workshops	8

Independent study hours
Independent study	70

Teaching staff

Staff member	Role
Ian Pratt-Hartmann	Unit coordinator

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