Full Title:Heuristics
Language of Instruction:English
Module Code:SWRD C8Z14
Credits: 5
Valid From:Semester 1 - 2014/15 ( September 2014 )
Module Delivered in No Programmes
Module Description:Students completing this module will have gained an understanding of some commonly used Artificial Intelligence algorithms and techniques including Decision Trees, Neural Networks, Fuzzy Logic, Finite State Machines, Swarm Intelligence and Genetic Algorithms. The students will also have programmed some algorithms using Lisp. 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. Construct Decision Trees.
  2. Design and use Neural Networks.
  3. Apply the process of fuzzification to particular problems.
  4. Implement a Finite State Machine.
  5. Understand the principle aspects of Genetic Algorithms.
  6. Design and apply Swarm Intelligence techniques.
  7. Program algorithms in Lisp.

Module Content & Assessment

Indicative Content
Decision Trees
ID3 Algorithm
Neural Networks
Perceptrons, Multi-layer Perceptrons
Fuzzy Logic
Fuzzy Sets, Fuzzy Set Operators, Fuzzy Rules
Finite State Machines
Genetic Algorithms
Swarm Intelligence
Ant Colony Optimization
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
Continuous Assessment The assessment criteria for continuous assessments will be concerned with the students' demonstrated understanding and application of some of the algorithms presented. Students' ability to evaluate solutions will also be assessed. 1,7 30.00 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 1,2,3,4,5,6,7 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   2.00 Every Week 2.00
Independent Study   4.00 Every Week 4.00
Total Weekly Learner Workload 9.00
Total Weekly Contact Hours 3.00
This course has no Part Time workload.
Supplementary Book Resources
  • Russell, S. & Norvig, P. 2003, Artificial Intelligence:A modern approach, 2nd ed Ed., Prentice-Hall [ISBN: 0137903952]
  • Buckland, Mat. 2005, Programming Game AI by Example, Wordware Publishing [ISBN: 1-55622-078-2]
  • Bonabeau,Dorigo &Theraulaz. 1999, Swarm Intelligence - From Natural to Artificial Systems, Oxford University Press [ISBN: 0-19-513159-2]
  • Tanimoto, S. 1995, The Elements of Artificial Intelligence Using Common Lisp, 2nd ed Ed., W.H. Freeman & Co [ISBN: 0-7167-8269-3]
This module does not have any article/paper resources
This module does not have any other resources