1. Introduction to AI Module – CS289 Introduction to Artificial Intelligence – CS289 04 th September 2006 Dr Bogdan L. Vrusias b.vrusias@surrey.ac.uk

2. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 20062 Fundamental Questions 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?

3. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 20063 CS289 Aims The aim of this module is: –This module aims 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.

4. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 20064 CS289 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 an “intelligent” system.

5. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 20065 CS289 Content I Knowledge-Based Intelligent Systems –Artificial intelligence from the ‘Dark Ages’ to knowledge-based systems –What is knowledge? –Knowledge representation techniques –Rules as a knowledge representation technique and Expert Systems

6. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 20066 CS289 Content II Uncertainty Management in Expert Systems –Introduction to uncertainty –Bayesian reasoning –Certainty factors theory and evidential reasoning

7. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 20067 CS289 Content III Fuzzy Expert Systems –Fuzzy sets and linguistic variables and hedges –Fuzzy inference for building a fuzzy expert system

8. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 20068 CS289 Content IV Machine Learning –Introduction to learning –Decision Trees –Introduction to Artificial Neural Networks –Introduction to Evolutionary Computation

9. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 20069 CS289 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 discover

10. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200610 Assessment Pattern Unit(s) of AssessmentWeighting Towards Module Mark (%) Coursework25 Verbal Examination (based on the coursework)15 Examination60 Qualifying Condition(s) A weighted aggregate mark of 40% is required to pass the module.

11. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200611 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 10-page (max) report on his or her study, not exceeding 3000 words. –More details will be give at appropriate time.

12. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200612 Methods of Teaching/Learning The module will consist of 24 hours of lectures, and 6 practical tutorial hours. NOTE: Attending lectures is VERY important!

13. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200613 On-line Resources CS289 main resource –http://www.cs.surrey.ac.uk/teaching/cs289 NOTE: Make sure you check the module website regularly! The WWWW (i.e http://www.google.com !!!)

14. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200614 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: 0321204662.

15. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200615 Selected Texts II Other recommended books are: –Luger, G.F (2004) Artificial Intelligence: Structures & Strategies for Complex Problem Solving (Fifth Edition). London: Addison-Wesley, ISBN: 0321263189. –Callan, Rob (2003), Artificial Intelligence, Basingstoke, Hampshire, UK, Palgrave MacMillan, ISBN: 0333801369. –Winston, Patrick H. (1992), Artificial Intelligence (Third Edition), Reading (MASS): Addison-Wesley Publishers Co, ISBN: 0201533774.

16. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200616 Learning contract – for us all Punctuality No disruption of other’s learning Mobile phones! Availability (office 06BB02): –Tuesdays 14:00 - 16:00 Communication: email and the student hours Fun

17. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200617 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?

18. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200618 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.

19. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200619 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.

20. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200620 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:

21. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200621 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.

22. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200622 Turing Imitation Game – Phase 2 Second Phase 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.

23. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200623 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.

24. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200624 Some AI Examples Please check the following websites on your free time: –http://www.generation5.org/jdk/demos.asp –http://www.aridolan.com/ofiles/eFloys.html –http://www.aridolan.com/ofiles/iFloys.html –http://www.arch.usyd.edu.au/~rob/#applets –http://www.softrise.co.uk/srl/old/caworld.html –http://people.clarkson.edu/~esazonov/neural_fuzzy/loadsway/LoadSway.htm –http://www.iit.nrc.ca/IR_public/fuzzy/FuzzyTruck.html –http://www.pandorabots.com/pandora/talk?botid=f5d922d97e345aa1

25. Introduction to AI Module – CS289 4 th September 2006Bogdan L. Vrusias © 200625 Closing Questions??? Remarks??? Comments!!! Evaluation!