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CSNB234ARTIFICIAL INTELLIGENCE

Chapter: Part IBackground & History of AI

Chapter: Part IBackground & History of AI

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Natural Vs. Artificialintelligence

What is Natural Intelligence? Human intelligence The word ‘natural’ is normally omitted

What is Artificial Intelligence? Intelligences posses by machines

What is IQ?

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IQ of a personis measured by

Mental AgeIQ = -------------------------------- * 100 Chronological Age

Mental AgeIQ = -------------------------------- * 100 Chronological Age

E.g. if a 20 years old person undergoes an IQ test and the examiner determines his mental age as 18, then his IQ is 90 ------------------> below average!

This is the simplest formula that works well

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AI can be defined as the attempt to get real

machines to behave like the ones in the movies.

First glance at the definition of AI

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AI programs Vs. Traditional programs

Traditional Program =____________ + ____________

AI Program = _____________ + _____________

Main difference Heuristics vs. Algorithmic

Traditional Program =____________ + ____________

AI Program = _____________ + _____________

Main difference Heuristics vs. Algorithmic

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The AI Theorists

Father of “Artificial Intelligence is Alan Turing

Other AI Theorists: McDermott, Patrick Winston, Newell,

Simon, Rosenblatt& more (perform an internet

search)..

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Warren McCulloch (Columbia University) Human Brain

Claude Shannon (Bell Lab)Boolean Algebra

Norbert Wiener John McCarthy (Dartmouth College) Marvin Minsky (Harvard U)

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Alan Turing(1912-1954)

He is the father of AI

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AI : History 1956: Dartmouth Conference - proposed launch of Joint

Research on AI. John McCarthy, Marvin Minsky, Claude Shannon among the

attendees.

1960s: Focus on knowledge bases started. Areas of interests are chess games, theorem proving and language translation. Lisp developed by John McCarthy.

1963: Newell & Simon built General Problem Solver (GPS).

1965: DENDRAL developed by Feigenbaum at Stanford University.

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1970s: MYCIN developed at Stanford University, utilised production rules.

1972: PROLOG developed by Alain Colmerauer at University of Marseilles.

1981: ICOT (Institute of New Generation Computer Technology).

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Symbolic Processing

It is a branch of Computer Science that deals with symbolic, non-algorithmic methods of problem solving.

Heuristics

It is the branch of Computer Science that deals with ways of representing knowledge using symbols rather than numbers and with rules-of-thumb for processing information.

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Heuristics and Heuristic programming

Heuristics– Developed through intuition, experience & judgment.– Do not represent (our) knowledge of design, rather,

they represent guidelines through which a system may be operated.

– Often called “Rules of thumb”.

Characteristics Screening Filtering Pruning

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HEURISTIC PROGRAMMING

• Should not be confused with computer programming.

• A program is a solution; programming is a procedure for obtaining a solution.

• Thus, heuristic programming is a procedure for finding the solution to a model consisting of “heuristics”.

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LANGUAGE LEVELS FOR AI PROBLEM SOLVING

Two Levels of Abstraction: Symbol level Knowledge level

Symbol Level:– concerns with the particular formalisms used to

represent knowledge such as logic or production rules.

– concerns with the structures used to organize knowledge.

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Knowledge Level:

What queries / questions will be asked? How new knowledge can be added or

updated? What objects and relations are

necessary? Can the system reasons despite of

incompleteness of information?

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Essential requirements for an AI language

Support of Symbolic Computation– implementation of a set of operation on

symbolic rather than numeric data.

– predicate calculus is a powerful tool for constructing qualitative descriptions of a domain.

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Flexibility of Control– Rule-based systems being the most

important paradigm for building AI programs.

– AI cannot be achieved through step-by-step execution of a fixed sequence of instructions .

– Production rules can be fired in virtually any order (i.e. not step-by-step) in response to a given situation.

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Support of Exploratory Programming Methodologies– AI programs seldom respond to standard

software approaches such as top-down design, stepwise refinement.

– This is due to the nature of AI problems that they could be started & tested without having to completely produce the final specification.

– In other words, most AI programs are initially poorly specified.

– AI programming is inherently exploratory; the program is the vehicle through which we explore the problem area (domain) and discover solution strategies.

Support of Exploratory Programming Methodologies– AI programs seldom respond to standard

software approaches such as top-down design, stepwise refinement.

– This is due to the nature of AI problems that they could be started & tested without having to completely produce the final specification.

– In other words, most AI programs are initially poorly specified.

– AI programming is inherently exploratory; the program is the vehicle through which we explore the problem area (domain) and discover solution strategies.

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Late Binding & Constraint Propagation

– Often, the problems addressed by AI program (such as Prolog program) require that the values of certain entities to remain unknown until sufficient information is gathered to determine the assignment.

– As constraints are accumulated, the set of possible values is reduced, ultimately converging on a solution.

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Clear and Well-defined Semantics– Traditional computer languages are too

complex in its programming constructs and semantic definitions. They are not subject to self-proof.

– This could be achieved by developing new languages that do not (to certain extent) conform to the architecture underlying von Neumann computer and be on the foundation of mathematical formalisms such as logic (Prolog).

Clear and Well-defined Semantics– Traditional computer languages are too

complex in its programming constructs and semantic definitions. They are not subject to self-proof.

– This could be achieved by developing new languages that do not (to certain extent) conform to the architecture underlying von Neumann computer and be on the foundation of mathematical formalisms such as logic (Prolog).

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AI Systems Development

Knowledge and expertise slowly building up..

Immature but can be used (tested)

This methodology is called _____________

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CCSB354ARTIFICIAL INTELLIGENCE

Chapter 1: Part IIIntroduction to AI

Chapter 1: Part IIIntroduction to AI

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Can a machine think?

Can be answered by the following “tests” for machine (i.e. the program/software)

The Alan Turing Test Alan Turing (father of AI)

Revised Turing Test ELIZA (By Joseph Weizenbaum of MIT)

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Artificial Intelligence

Definition AI is the study of how to make computers do things at

which, at the moment, people are better. What computer can do better than people?

Numerical computation: Fast & accurate Information storage: Voluminous amounts Repetitive operations : Not getting bored (??)

However, these are mechanical mindless activities, and thus cannot be regarded as ‘intelligent’ tasks

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What people can do better than computers?

Activities that involve intelligence include: Understanding Common sense reasoning Natural language processing and generation Planning & Design Learning (e.g. from mistakes, by analogy, by

experience or examples) Emotions

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What is “intelligence”?

It has the ability To respond to situation very flexibly To make sense out of ambiguous messages To recognize the relative importance of different

elements of a situation

It is the part of Computer Science that concerned with the designing of intelligent computer systems, that is, systems that exhibit characteristics we associate with intelligence

in human behavior.

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Differences between AI and Conventional Systems

Conventional SystemsProceduralNumerical

processingAlgorithmicRigid syntax

AI Systems

Declarative Symbolic processing Heuristic

programming More natural syntax

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Areas of AI Research Automated reasoning Expert systems Natural language processing Speech recognition Computer vision Robotics Automatic programming Data mining Optimization

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Applied Fields of AIApplied Fields of AI

ExpertSystems

NaturalLanguageProcessing

RoboticsComputer

Vision

ComputerizedSpeech

RecognitionMachineLearning

AI

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Other AI branches:

1.Intelligent software agents2.Machine learning3.Neural networks4.Evolutionary algorithms5.Semantic technology

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Class Exercise 1

Some characteristics of “intelligence” are: Be able to identify d_________ between situations. Be able to identify w______________ in a situation. Be able to respond to a situation very f________. Be able to l____ from experience. Be able to p__________ and make events cohere. Be able to see s__________ out of complexity. Be able to ad______, j ______, and j________. Be able to handle un___________ of information/data.

Some characteristics of “intelligence” are: Be able to identify d_________ between situations. Be able to identify w______________ in a situation. Be able to respond to a situation very f________. Be able to l____ from experience. Be able to p__________ and make events cohere. Be able to see s__________ out of complexity. Be able to ad______, j ______, and j________. Be able to handle un___________ of information/data.

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Class Exercise 2

Name some features of “Artificial Intelligence”.

The use of large amount of d________- s________ knowledge in its problem solving.

Solutions may be just g____- e________ (i.e. neither exact nor optimal).

Q_______ and S________ aspects are in concern (not numerical analysis).

Non-a____________. H_________ programming is the key to software

intelligence.

Name some features of “Artificial Intelligence”.

The use of large amount of d________- s________ knowledge in its problem solving.

Solutions may be just g____- e________ (i.e. neither exact nor optimal).

Q_______ and S________ aspects are in concern (not numerical analysis).

Non-a____________. H_________ programming is the key to software

intelligence.

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The Birth of AI (I)

The Turing TestThis test was invented by Alan Turing (1912-

1954) It was first described in his 1950 article Computing

machinery and intelligence (Mind, Vol. 59, No. 236, pp. 433-460)

An interrogator is connected to one person and one machine via a terminal, and therefore can't see his counterparts.

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The Birth of AI (II) The Turing Test

His task is to find out which of the two candidates is the machine, and which is human only by asking them questions.

If the interrogator cannot make a decision within a certain time (Turing proposed five minutes, but the exact amount of time is generally considered irrelevant),

the machine is considered to be intelligent.

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If the computer succeeds in fooling the interrogator, i.e. the interrogator cannot distinguish the machine from the human,then, Turing argues, the machine may be assumed to be “intelligent”

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