Full Title:Artificial Intelligence
Language of Instruction:English
Module Code:COMP C8Z12
 
Credits: 5
Valid From:Semester 1 - 2014/15 ( September 2014 )
Module Delivered in 2 programme(s)
Module Description:This module aims to introduce some fundamental concepts and techniques of Artificial Intelligence including Knowledge Representation, Search, Machine Learning and Expert Systems. The students will also program algorithms using Prolog. As a result the student should have the ability to identify and implement appropriate techniques when problem solving.
Learning Outcomes:
On successful completion of this module the learner should be able to
  1. Critically analyze traditional AI knowledge representation methods
  2. Use appropriate search methods in problem solving
  3. Apply appropriate machine learning algorithms to problems
  4. Identify and explain the various stages in the development of an Expert System
  5. Program Artificial Intelligence algorithms in Prolog
 

Module Content & Assessment

Indicative Content
Knowledge Representation
­Semantic Networks, Frames, Propositional Logic, Predicate Calculus, ­Inference, Rule Based Systems, Forward/Backward Chaining
Search methods
­Breath First, Depth First, Heuristics, Hill Climbing, Best First, A*, ­Minimax, Alpha-Beta Pruning
Machine Learning
Decision Tree Induction and the ID3 Algorithm
Expert Systems
Architecture, Rule Based Systems, Knowledge Engineering
Programming in Prolog
­Basics of Prolog, Backtracking, Unification
Assessment Breakdown%
Course Work30.00%
End of Module Formal Examination70.00%

Full Time

Course Work
Assessment Type Assessment Description Outcome addressed % of total Marks Out Of Pass Marks Assessment Date Duration
Practical/Skills Evaluation Students are required to write a program using general knowledge representation principles. 1,5 15.00 0 0 n/a 0
Practical/Skills Evaluation Students are required to write a program using general problem solving strategies. 2,5 15.00 0 0 n/a 0
No Project
No Practical
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Marks Out Of Pass Marks Assessment Date Duration
Formal Exam End-of-Semester Final Examination 1,2,3,4,5 70.00 0 0 End-of-Semester 0
Reassessment Requirement
A repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

DKIT reserves the right to alter the nature and timings of assessment

 

Module Workload & Resources

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture   2.00 Every Week 2.00
Practical   1.00 Every Week 1.00
Directed Reading No Description 2.00 Every Week 2.00
Independent Study   3.00 Every Week 3.00
Total Weekly Learner Workload 8.00
Total Weekly Contact Hours 3.00
This course has no Part Time workload.
Resources
Recommended Book Resources
  • ­Russell, S. & Norvig, P. 2010, Artificial Intelligence: A modern approach, 3e Ed., Pearson [ISBN: 0132071487]
  • Bratko, Ivan 2011, Prolog Programming for Artificial Intelligence, 4e Ed., Addison Wesley [ISBN: 0201403757]
This module does not have any article/paper resources
Other Resources

Module Delivered in

Programme Code Programme Semester Delivery
DK_KCOMP_8 Bachelor of Science (Honours) in Computing 7 Elective
DK_KGMDV_8 Bachelor of Science (Honours) in Computing in Games Development 7 Elective