CE213-5-AU-CO:
Artificial Intelligence

The details
2019/20
Computer Science and Electronic Engineering (School of)
Colchester Campus
Autumn
Undergraduate: Level 5
Current
Thursday 03 October 2019
Saturday 14 December 2019
15
29 April 2019

 

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

 

(none)

Key module for

BSC C831 Cognitive Science,
BSC C832 Cognitive Science (Including Year Abroad),
BSC C833 Cognitive Science (Including Placement Year),
BSC G610 Computer Games,
BSC G612 Computer Games (Including Year Abroad),
BSC I610 Computer Games (Including Placement Year),
BSC 5B43 Statistics (Including Year Abroad),
BSC 9K12 Statistics,
BSC 9K13 Statistics (Including Placement Year),
BSC 9K18 Statistics (Including Foundation Year),
BSC I1G3 Data Science and Analytics,
BSC I1G3CE Data Science and Analytics,
BSC I1GB Data Science and Analytics (Including Placement Year),
BSC I1GBCE 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)

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

. Introduction
What AI is and is not
The debate about whether AI is possible

. 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
Hill climbing
Game playing - minimax search
Means ends analysis
Implementing search

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

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

. Intelligent agents
Reactive v. deliberative agents
Subsumption architectures for purely reactive agents

Learning and teaching methods

Lectures and Classes

Bibliography

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 Description Deadline Weighting
Coursework Progress Test - Week 6 25%
Coursework Progress Test 2 - Week 11 25%
Coursework Assignment 1 - Programming Exercise 19/11/2019 50%
Exam 120 minutes during Summer (Main Period) (Main)

Overall assessment

Coursework Exam
40% 60%

Reassessment

Coursework Exam
40% 60%
Module supervisor and teaching staff
Professor John Gan
CSEE 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
No

External examiner

No external examiner information available for this module.
Resources
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).

 

Further information

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