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CSNB234 ARTIFICIAL INTELLIGENCE

CSNB234 ARTIFICIAL INTELLIGENCE. Chapter: Part I Background & History of AI. Natural Vs. Artificial intelligence. 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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CSNB234 ARTIFICIAL INTELLIGENCE

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  1. CSNB234ARTIFICIAL INTELLIGENCE Chapter: Part I Background & History of AI COIT, UNITEN

  2. Natural Vs. Artificial intelligence • What is Natural Intelligence? • Human intelligence • The word ‘natural’ is normally omitted • What is Artificial Intelligence? • Intelligences posses by machines • What is IQ? COIT, UNITEN

  3. IQ of a personis measured by Mental Age IQ = -------------------------------- * 100 Chronological Age This is the simplest formula that works well 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! COIT, UNITEN

  4. 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 COIT, UNITEN

  5. AI programs Vs. Traditional programs • Traditional Program =____________ + ____________ • AI Program = _____________ + _____________ • Main difference • Heuristics vs. Algorithmic COIT, UNITEN

  6. The AI Theorists • Father of “Artificial Intelligence is • Alan Turing • Other AI Theorists: • McDermott, Patrick Winston, Newell, Simon, Rosenblatt • & more (perform an internet search).. COIT, UNITEN

  7. Warren McCulloch (Columbia University) • Human Brain • Claude Shannon (Bell Lab) • Boolean Algebra • Norbert Wiener • John McCarthy (Dartmouth College) • Marvin Minsky (Harvard U) COIT, UNITEN

  8. Alan Turing(1912-1954) He is the father of AI COIT, UNITEN

  9. 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. COIT, UNITEN

  10. 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). COIT, UNITEN

  11. 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. COIT, UNITEN

  12. 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 COIT, UNITEN

  13. 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”. COIT, UNITEN

  14. 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. COIT, UNITEN

  15. 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? COIT, UNITEN

  16. 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. COIT, UNITEN

  17. 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. COIT, UNITEN

  18. 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. COIT, UNITEN

  19. 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. COIT, UNITEN

  20. 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). COIT, UNITEN

  21. AI Systems Development Immature but can be used (tested) Knowledge and expertise slowly building up.. This methodology is called _____________ COIT, UNITEN

  22. CCSB354ARTIFICIAL INTELLIGENCE Chapter 1: Part II Introduction to AI COIT, UNITEN

  23. 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) COIT, UNITEN

  24. 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 COIT, UNITEN

  25. 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 COIT, UNITEN

  26. 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. COIT, UNITEN

  27. Conventional Systems Procedural Numerical processing Algorithmic Rigid syntax Differences between AI and Conventional Systems • AI Systems • Declarative • Symbolic processing • Heuristic programming • More natural syntax COIT, UNITEN

  28. Areas of AI Research • Automated reasoning • Expert systems • Natural language processing • Speech recognition • Computer vision • Robotics • Automatic programming • Data mining • Optimization COIT, UNITEN

  29. Applied Fields of AI AI Natural Language Processing Computerized Speech Recognition Expert Systems Computer Vision Machine Learning Robotics COIT, UNITEN

  30. Other AI branches: Intelligent software agents Machine learning Neural networks Evolutionary algorithms Semantic technology COIT, UNITEN

  31. 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. COIT, UNITEN

  32. 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. COIT, UNITEN

  33. The Birth of AI (I) • The Turing Test • This 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. COIT, UNITEN

  34. 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. COIT, UNITEN

  35. Pening aku ni... Siapa yang menjawab ini? 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” COIT, UNITEN

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