Computer Science and Electronic Engineering (School of)
Undergraduate: Level 5
Thursday 03 October 2019
Saturday 14 December 2019
29 April 2019
Requisites for this module
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)
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
. 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
. 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
. 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 2 - Week 11
||Assignment 1 - Programming Exercise
||120 minutes during Summer (Main Period) (Main)
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
No external examiner information available for this module.
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).
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