Back to Module List

CMPSMA24 - ARTIFICIAL INTELLIGENCE AND ALGORITHMICS

Module Code:
CMPSMA24
Department:
Computing Sciences
Credit Value:
20
Level:
M
Organiser:
Mr. Pierre Chardaire
This module introduces the student to core techniques in Artificial Intelligence and some topics in algorithmics. Topics to be covered include state space, search techniques, algorithmic paradigms, NP-completeness, metaheuristics, logic and knowledge representation and expert systems.

SWI - Prolog on laboratory machines


Recommended reading:

  • Luger,G.F. (2005) Artificial Intelligence: Structures and Strategies for Complex Problem Solving, fifth edition, Addison-Wesley
  • Russell,S., Norwig, P.(2003) Artificial Intelligence: a modern approach, (2e) Prentice Hall
  • Dean,T., Allen, J. and Aloimonos, Y.(1996) Artificial Intelligence: Theory and Practice, Addison-Wesley

Submission:

Written coursework should be submitted by following the standard CMP practice. Students are advised to refer to the Guidelines and Hints on Written Work in CMP.

Deadlines:

If coursework is handed in after the deadline day or an agreed extension:

Work submitted Marks deducted
After 15:00 on the due date and before 15:00 on the day following the due date 10 marks
After 15:00 on the second day after the due date and before 15:00 on the third day after the due date 20 marks
After 15:00 on the third day after the due date and before 15:00 on the 20th day after the due date.  All the marks the work merits if submitted on time (ie no marks awarded) 
After 20 working days Work will not be marked and a mark of zero will be entered


Saturdays and Sundays will NOT be taken into account for the purposes of calculation of marks deducted.

All extension requests will be managed through the LTS Hub. A request for an extension to a deadline for the submission of work for assessment should be submitted by the student to the appropriate Learning and Teaching Service Hub, prior to the deadline, on a University Extension Request Form accompanied by appropriate evidence. Extension requests will be considered by the appropriate Learning and Teaching Service Manager in those instances where (a) acceptable extenuating circumstances exist and (b) the request is submitted before the deadline. All other cases will be considered by a Coursework Coordinator in CMP.

For more details, including how to apply for an extension due to extenuating circumstances download Submission for Work Assessment (PDF, 39KB)
 

Plagiarism:

Plagiarism is the copying or close paraphrasing of published or unpublished work, including    the work of another student; without due acknowledgement. Plagiarism is regarded a serious offence by the University, and all cases will be investigated. Possible consequences of plagiarism include deduction of marks and disciplinary action, as detailed by UEA's Policy on Plagiarism and Collusion.


 


Module specific:

On completion of the module, students should be able to:

  • Solve simple propositional and predicate logic problems
  • Understand the need for search and be able to explain simple search techniques
  • Understand how AI techniques are used in game playing
  • Write programs in prolog
  • Manipulate probabilities and analyse Bayesian networks
  • Describe a variety of machine learning techniques and assess their applicability for a given scenario
  • Understand the principles and use of both expert system and case based reasoning

On completion of this unit students should have achieved:

  • Competence in logical problem solving skills
  • Understanding of declarative programming
  • Improved  ability to develop mathematical models

Total hours: 32

Lectures: 20 hours (with provisional weekly schedule)

  1. Propositional logic , predicate logic

  2. Resolution theorem proving

  3. State based search  methods

  4. Logic programming, prolog

  5. Logic programming, prolog

  6. Problem reduction based search methods

  7. Mathematical modelling and NP-completeness

  8. Knowledge representation 

  9. Introduction to machine learning techniques

  10. Artificial neural networks, Bayesian networks 

  11. Expert systems and Case based reasoning

Laboratory work: 4 hours

Topics:

  1. Introduction to prolog (4 weeks)
  2. Neural networks and bayesian networks (3 weeks)

Prolog programming exercise (set in week 5, due week 10, returned week 12), makes up 40% of the marks for the module. A three hour exam makes up the remaining 60% of the marks for the module.