CE213-5-SP-CO:
Introduction to Artificial Intelligence

The details
2024/25
Computer Science and Electronic Engineering (School of)
Colchester Campus
Spring
Undergraduate: Level 5
Current
Monday 13 January 2025
Friday 21 March 2025
15
24 October 2023

 

Requisites for this module
(none)
(none)
(none)
(none)

 

CE345

Key module for

BSC G610 Computer Games,
BSC G612 Computer Games (Including Year Abroad),
BSC G620 Computer Games (Including Foundation Year),
BSC I610 Computer Games (Including Placement Year),
BSC I1G3 Data Science and Analytics,
BSC I1GB Data Science and Analytics (Including Placement Year),
BSC I1GC Data Science and Analytics (Including Year Abroad),
BSC I1GF Data Science and Analytics (Including Foundation Year),
BENGH615 Robotic Engineering,
BENGH616 Robotic Engineering (Including Year Abroad),
BENGH617 Robotic Engineering (Including Placement Year),
BENGH618 Robotic Engineering (Including Foundation Year),
BENGH169 Neural Engineering with Psychology,
BENGH170 Neural Engineering with Psychology (including Placement Year),
BENGH171 Neural Engineering with Psychology (including Year Abroad),
BENGH172 Neural Engineering with Psychology (Including Foundation Year),
BSC H167 Neural Technology with Psychology,
BSC H168 Neural Technology with Psychology (including Year Abroad),
BSC H176 Neural Technology with Psychology (including Placement Year),
BSC H717 Robotics,
BSC H718 Robotics (including Placement Year),
BSC H719 Robotics (including Year Abroad),
BSC I400 Artificial Intelligence,
BSC I401 Artificial Intelligence (Including Foundation Year),
BSC I402 Artificial Intelligence (including Placement Year),
BSC I403 Artificial Intelligence (including Year Abroad)

Module description

This module provides an introduction to three fundamental areas of artificial intelligence: search, knowledge representation and learning. These underpin all more advanced areas of artificial intelligence and are of central importance to related fields such as computer games and robotics.

Within each area, a range of methodologies and techniques are presented; emphasis is placed on understanding their strengths and weaknesses and hence on assessing which is most suited to a particular task. The module also provides an introduction to the philosophical arguments about the possibility of a machine being able to think. It concludes with a brief overview of systems based on interacting intelligent agents.

Module aims

This module aims to provide an introduction to three fundamental areas of artificial intelligence: search, knowledge representation and learning.

Module learning outcomes

After completing this module, students will be expected to be able to:

1. Explain and criticise the arguments that have been advanced both for and against the possibility of artificial intelligence.
2. Explain and implement standard blind and heuristic search procedures, demonstrate an understanding of their strengths and weaknesses and of how they may be applied to solve well-defined problems.
3. Explain the operation of standard production system interpreters, and demonstrate an understanding of their relative merits.
4. Explain the operation of a range of established machine learning procedures and demonstrate an understanding of the types of problems for which they are appropriate.
5. Demonstrate an understanding of the agent-oriented approach to artificial intelligence, and explain how a multi-agent system of purely reactive agents may be built using a subsumption architecture.

Module information

Outline Syllabus

1. Introduction
What is AI?
Is AI possible?
How is AI possible?
AI applications

2. Solving problems by searching
State space representation
Search trees and graphs
Blind search strategies - depth first, breadth first and iterative deepening
Heuristic search - greedy search and A* search
Game playing - minimax search, Monte-Carlo tree search

3. Using knowledge to solve problems
The importance of domain knowledge
Rule based systems (Expert systems)
Forward chaining rule interpreters
Backward chaining rule interpreters

4. Acquiring knowledge - machine learning
Decision tree induction
Neural networks - back propagation
Reinforcement learning - Q algorithm
Genetic algorithms

5. Intelligent agents
Reactive agents
Subsumption architectures for purely reactive agents
Multi-agent systems

Learning and teaching methods

Lectures and Classes

Bibliography

The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Progress Test 1    25% 
Coursework   Progress Test 2    25% 
Coursework   Assignment 1: Programming Exercise    50% 
Exam  Main exam: In-Person, Open Book (Restricted), 120 minutes during Summer (Main Period) 
Exam  Reassessment Main exam: In-Person, Open Book (Restricted), 120 minutes during September (Reassessment Period) 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
40% 60%

Reassessment

Coursework Exam
40% 60%
Module supervisor and teaching staff
Dr Vishal Singh, email: v.k.singh@essex.ac.uk.
Dr Vishal Singh
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770

 

Availability
Yes
No
Yes

External examiner

Dr Iain Phillips
Loughborough University
Resources
Available via Moodle
Of 580 hours, 0 (0%) hours available to students:
580 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).

 

Further information

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