1. Introduction to Artificial Intelligence – CS364
06th September 2005
Dr Bogdan L. Vrusias

2. Fundamental Question of AI
(Alan Turing asked:)
Is there thought without experience?
Is there mind without communication?
Is there language without living?
Is there intelligence without life? …
Can machines think?
06th September 2005
Bogdan L. Vrusias © 2005

3. CS364 Aims The aim of this module is:
To demonstrate a variety of techniques for capturing human knowledge and represent it in a computer in a way that enables the machine to reason over the data represented and mimic the human ability to deal with incomplete or uncertain data.
06th September 2005
Bogdan L. Vrusias © 2005

4. CS364 Outcomes At the end of the module students should be able to:
Describe methods for acquiring human knowledge.
Evaluate which of the acquisition methods would be most appropriate in a given situation.
Describe techniques for representing acquired knowledge in a way that facilitates automated reasoning over the knowledge.
Categorise and evaluate AI techniques according to different criteria such as applicability and ease of use, and intelligently participate in the selection of the appropriate techniques and tools, to solve simple problems.
Use the presented techniques in practice to develop a fuzzy logic system.
06th September 2005
Bogdan L. Vrusias © 2005

5. CS364 Content I Knowledge-Based Intelligent Systems
Intelligent machines and what they can do.
Artificial intelligence from the ‘Dark Ages’ to knowledge-based systems
What is knowledge?
Knowledge representation techniques
Rules as a knowledge representation technique
06th September 2005
Bogdan L. Vrusias © 2005

6. CS364 Content II Uncertainty Management in Expert Systems
Introduction to uncertainty
Bayesian reasoning
Certainty factors theory and evidential reasoning
Comparison of Bayesian reasoning and certainty factors
06th September 2005
Bogdan L. Vrusias © 2005

7. CS364 Content III Fuzzy Expert Systems Introduction to fuzzy thinking
Fuzzy sets
Linguistic variables and hedges
Operations of fuzzy sets
Fuzzy rules
Fuzzy inference
Building a fuzzy expert system
06th September 2005
Bogdan L. Vrusias © 2005

8. CS364 Content IV Machine Learning Introduction to learning
Introduction to Artificial Neural Networks
Introduction to Evolutionary Computation
06th September 2005
Bogdan L. Vrusias © 2005

9. CS364 Content V Knowledge Engineering and Data Mining
Introduction to knowledge engineering
How to find the tools that will work for my problem
Data mining and knowledge discovery
06th September 2005
Bogdan L. Vrusias © 2005

10. Methods of Assessment
The assignment (40 marks) will be based on one item of coursework and an oral exam:
70% (28 marks) – Assignment
Available online on Monday week 5 (3rd October)
Collected on Monday 12:00am, week 10 (7th November)
30% (12 marks) – Oral Examination (VIVA)
15min oral examination of each student individually.
Starting week 11 (14th November)
A two-hour written examination (60 marks).
Students must answer 2 questions out of 3, with each question carrying 30 marks.
06th September 2005
Bogdan L. Vrusias © 2005

11. Coursework
The students are expected to participate in a group project focused on studying the architecture and behaviour of an fuzzy logic system.
Students may use a pre-existing program (shell) or write their own.
The department will provide the Matlab Fuzzy Logic tool,
but, there are web sites which contain AI freeware and the students are expected to make the most of this freeware.
The student is expected to write an individual 8-page report (including diagrams and bibliography) on his or her study not exceeding 2000 words.
More details will be give at the time.
06th September 2005
Bogdan L. Vrusias © 2005

12. Methods of Teaching/Learning
The module will consist of 30 hours of lectures, some of them practical tutorial hours.
NOTE: Attending lectures is VERY important!
06th September 2005
Bogdan L. Vrusias © 2005

13. On-line Resources CS364 Related
The WWWW (i.e !!!)
06th September 2005
Bogdan L. Vrusias © 2005

14. Selected Texts
The main course book for this module that contains most of the theoretical material is:
Negnevitsky, Michael (2004), Artificial Intelligence – A Guide to Intelligent Systems (Second Edition), Harlow, UK, Addison Wesley, ISBN:
06th September 2005
Bogdan L. Vrusias © 2005

15. Selected Texts II Recommended books are:
Luger, G.F (2004) Artificial Intelligence: Structures & Strategies for Complex Problem Solving (Fifth Edition). London: Addison-Wesley, ISBN:
Callan, Rob (2003), Artificial Intelligence, Basingstoke, Hampshire, UK, Palgrave MacMillan, ISBN:
Winston, Patrick H. (1992), Artificial Intelligence (Third Edition), Reading (MASS): Addison-Wesley Publishers Co, ISBN:
06th September 2005
Bogdan L. Vrusias © 2005

16. Learning contract – for us all
Punctuality
No disruption of other’s learning
Mobile phones
Availability – I am available on:
Tuesdays 14: :00
Thursdays 14: :00.
Communication: and the student hours
Fun
06th September 2005
Bogdan L. Vrusias © 2005

17. Discussion Can machines think? Can machines see?
How does a human mind work? Is it magic?
Can non-humans have minds?
Can machines replace a human worker?
Are intelligent machines good or bad for humans?
Would you trust one?
06th September 2005
Bogdan L. Vrusias © 2005

18. What is Intelligence?
Intelligence is the ability to understand and learn things.
Intelligence is the ability to think and understand instead of doing things by instinct or automatically.
(Essential English Dictionary, Collins, London, 1990).
Intelligence is the ability to learn and understand, to solve problems and to make decisions.
06th September 2005
Bogdan L. Vrusias © 2005

19. What is Artificial Intelligence?
The goal of artificial intelligence (AI) as a science is to make machines do things that would require intelligence if done by humans.
AI is a branch of computing science that deals with the specification, design and implementation of information systems that have some knowledge related to the enterprise in which the information systems are situated.
Such systems are designed per se to be responsive to the needs of their end-users.
06th September 2005
Bogdan L. Vrusias © 2005

20. Turing Imitation Game
The British mathematician Alan Turing, over fifty years ago, inventing a game, the Turing Imitation Game.
The imitation game originally included two phases:
06th September 2005
Bogdan L. Vrusias © 2005

21. Turing Imitation Game – Phase 1
In the first phase, the interrogator, a man and a woman are each placed in separate rooms. The interrogator’s objective is to work out who is the man and who is the woman by questioning them. The man should attempt to deceive the interrogator that he is the woman, while the woman has to convince the interrogator that she is the woman.
06th September 2005
Bogdan L. Vrusias © 2005

22. Turing Imitation Game – Phase 2
In the second phase of the game, the man is replaced by a computer programmed to deceive the interrogator as the man did. It would even be programmed to make mistakes and provide fuzzy answers in the way a human would. If the computer can fool the interrogator as often as the man did, we may say this computer has passed the intelligent behaviour test.
Second Phase
06th September 2005
Bogdan L. Vrusias © 2005

23. Turing Remarks
By maintaining communication between the human and the machine via terminals, the test gives us an objective standard view on intelligence.
A program thought intelligent in some narrow area of expertise is evaluated by comparing its performance with the performance of a human expert.
To build an intelligent computer system, we have to capture, organise and use human expert knowledge in some narrow area of expertise.
06th September 2005
Bogdan L. Vrusias © 2005

24. AI Examples http://www.generation5.org/jdk/demos.asp
06th September 2005
Bogdan L. Vrusias © 2005

25. Closing Questions??? Remarks??? Comments!!! Evaluation!
06th September 2005
Bogdan L. Vrusias © 2005