Artificial IntelligenceArtificial IntelligenceModule – CS364Module – CS364

Introduction toIntroduction toArtificial Intelligence – CS364Artificial Intelligence – CS364

05th September 2006Dr Bogdan L. Vrusias

b.vrusias@surrey.ac.uk

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Fundamental Questions of AIFundamental 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?

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CS364 AimsCS364 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.

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CS364 OutcomesCS364 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.

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CS364 Content ICS364 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

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CS364 Content IICS364 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

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CS364 Content IIICS364 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

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CS364 Content IVCS364 Content IV• Machine Learning

– Introduction to learning– Introduction to Artificial Neural Networks– Introduction to Evolutionary Computation

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CS364 Content VCS364 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

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Assessment PatternAssessment PatternUnit(s) of Assessment Weighting Towards

Module Mark (%)

Coursework 25

Verbal Examination (based on the coursework) 15

Examination 60

Qualifying Condition(s) A weighted aggregate mark of 40% is required to pass the module.

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CourseworkCoursework• 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.

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Methods of Teaching/LearningMethods of Teaching/Learning• The module will consist of 26 hours of lectures, and 4

practical tutorial hours.

• NOTE: Attending lectures is VERY important!

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On-line ResourcesOn-line Resources• CS364 main resource

– http://www.cs.surrey.ac.uk/teaching/cs364

NOTE: Make sure you check the module website regularly!

• The WWWW (i.e http://www.google.com !!!)

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Selected TextsSelected 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.

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Selected Texts IISelected 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.

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Learning contract – for us allLearning 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

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DiscussionDiscussion• 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?

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What is Intelligence?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.

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What is Artificial Intelligence?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.

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Turing Imitation GameTuring 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:

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Turing Imitation Game – Phase 1Turing 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.

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Turing Imitation Game – Phase 2Turing 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.

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Turing RemarksTuring 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.

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Some AI ExamplesSome 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

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ClosingClosing

• Questions???• Remarks???• Comments!!!• Evaluation!