Computer Science and Electronic Engineering (School of)
Colchester Campus & Apprenticeship Location
Undergraduate: Level 5
Thursday 08 October 2020
Friday 18 December 2020
29 July 2020
Requisites for this module
BSC YHG1 Digital and Technology Solutions (Software Engineer)
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.
This module aims to provide an introduction to three fundamental areas of artificial intelligence: search, knowledge representation and learning.
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.
What AI is and is not
The debate about whether AI is possible
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
Means ends analysis
3. Using knowledge to solve problems
The importance of domain knowledge
Rule based systems
Forward chaining rule interpreters
Backward chaining rule interpreters
4. Acquiring knowledge - machine learning
Decision tree induction
Neural networks - back propagation
Clustering - k means algorithm
Reinforcement learning - Q algorithm
5. Intelligent agents
Reactive v. deliberative agents
Subsumption architectures for purely reactive agents
This module does not appear to have any essential texts. To see non-essential items, please refer to the module's reading list.
Assessment items, weightings and deadlines
|Coursework / exam
||Progress Test - Week 6
||Progress Test - Week 11
||Assignment 1 - Programming exercise
||120 minutes during Summer (Main Period) (Main)
Module supervisor and teaching staff
Prof John Gan, email: firstname.lastname@example.org.
Professor John Gan, Dr Vito De Feo
CSEE School Office, email: csee-schooloffice non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770
Dr Iain Phillips
Available via Moodle
Of 41 hours, 38 (92.7%) hours available to students:
3 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
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