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Artificial Intelligence


Lecturere: Sheheen A. Abdulkareem
Dept. of Computer Science
Faculty of Science
University of Dohuk
Sep, 2010
What is Intelligence?

• Simply, it defined as set of properties of the mind!
      • The properties include the ability to plan, solve problems, and
        reason.
• Simpler, is the ability to make right decision given a
  set of inputs and variety of possible action
      • The ability to learn or understand or to deal with new or trying
        situations!
      • The ability to apply knowledge to manipulate one's environment
        or to think abstractly as measured by objective criteria (as
        tests)!
What is AI?

• Not just making Computers, Robots, or agents
  acts like humans!
  – They should think like humans not like machines!
  – We don’t want them to make humans mistakes!
  – We want them to learn but not from the time 0!
• The key is the ability to “share” learned results
  (i.e. copy data/program) between computers.
Hmm!
So…
• AI is not just studying intelligent systems, but
  building them…

• Psychological approach: an intelligent system is
  a model of human intelligence!

• Engineering approach: an intelligent system
  solves a sufficiently difficult problem in a
  generalizable way!
The AI Semester Objectives
• Become familiar with AI techniques, including
  their implementations
  – be able to develop AI applications using Python!
• Understand the theory behind the techniques,
  knowing which techniques to apply when (and
  why)
• Become familiar with a range of applications of
  AI
  – We will focus on Agent-based Modelling and
    applying it using NetLogo software!
AI History?

• Gestation (the early 1950’s):
  – McCulloch and Pitts artificial neuron, Hebbian
    learning
  – Early learning theory, first neural network, Turing test

• Birth (1957):
  – The Logic Theorist
  – Name coined by McCarthy
  – Workshop at Dartmouth
Cont’d…

• Early enthusiasm, great expectations (1952-
  1969)
  – GPS, physical symbol system hypothesis
  – Geometry Theorem Prover (Gelertner), Checkers
    (Samuels)
  – Lisp (McCarthy), Theorem Proving (McCarthy),
    Microworlds (Minsky et. al.)
  – “neat” (McCarthy @ Stanford) vs. “scruffy” (Minsky
    @ MIT)
Cont’d…
• Dose of Reality (1966-1973)
   – Combinatorial explosion
• Knowledge-based systems (1969-1979)

• AI Becomes an Industry (1980-present)
   – Boom period 1980-88, then AI Winter
• Return of Neural Networks (1986-present)

• AI Becomes a Science (1987-present)
   – SOAR, Internet as a domain
What is AI (Again)?
• Systems that think like           • Systems that think
  humans!                             rationally!
   • Cognitive Modeling Approach       – Laws of Thought approach
   • The automation of activities      – The study of mental faculties
     that we associate with human        through the use of computational
     thinking...                         models.
• Systems that act like             • Systems that act rationally!
  humans!                             • Rational Agent Approach
   • Turing Test Approach             • The branch of CS that is
   • The art of creating machines       concerned with the automation of
     that perform functions that        intelligent behavior
     require intelligence when
     performed by people
Acting Humanly!

• Turning Machine: Introducing the concept of his
  universal abstract machine.
  – Simple and could solve any mathematical problem.
     • Turning test: if the machine could fool a human into
       thinking that it was also human, then it passed the
       intelligence test.

  Can Machines Think?
Acting Humanly, Cont’d…

• Operational test for intelligent behavior
  • The Imitation Game


• Problem!
  – The turning test is not reproducible, constructive, or
    amenable to mathematical analysis
Thinking Humanly!

• 1960’s cognitive revolution
• Requires scientific theories of internal activities
  of the brain
     • What level of abstraction? “Knowledge” or “Circuits”
     • How to validate?
        – Predicting and testing behavior of human subjects (top-down)
        – Direct identification from neurological data (bottom-up)

• Cognitive Science and Cognitive Neuroscience
     • Now distinct from AI
Thinking Rationally
• Normative (or prescriptive) rather than
  descriptive
• Aristotle: What are correct arguments / thought
  processes?
• Logic notation and rules for derivation for
  thoughts
• Problems:
     • Not all intelligent behavior is mediated by logical
       deliberation
     • What is the purpose of thinking? What thoughts should I
       have?
Acting Rationally

• Rational behavior
     • Doing the right thing


• What is the “right thing”?
     • That which is expected to maximize goal achievement,
       given available information


• We do many (“right”) things without thinking
     • Thinking should be in the service of rational action
Applied Areas of AI

•   Heuristic Search
•   Computer Vision
•   Adversarial Search (Games)
•   Fuzzy Logic
•   Natural Language Processing
•   Knowledge Representation
•   Planning
•   Learning
Concepts to be learned

• Problem-Solving
    – Uninformed Search
    – Informed Search
•   AI and Games
•   Machine Learning
•   Evolutionary Computation
•   Robotics and AI
•   Intelligent Agents and Agent-based Modeling
Semester Tools

• References as Textbooks:
  – Artificial Intelligence a system approach, by M. Tim
    Jones, 2008.

  – Artificial Intelligence A modern Approach, 3rd
    Edition, by Stuart J. Russell and Peter Norving, 2010.

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Lec1 introduction

  • 1. Artificial Intelligence Lecturere: Sheheen A. Abdulkareem Dept. of Computer Science Faculty of Science University of Dohuk Sep, 2010
  • 2. What is Intelligence? • Simply, it defined as set of properties of the mind! • The properties include the ability to plan, solve problems, and reason. • Simpler, is the ability to make right decision given a set of inputs and variety of possible action • The ability to learn or understand or to deal with new or trying situations! • The ability to apply knowledge to manipulate one's environment or to think abstractly as measured by objective criteria (as tests)!
  • 3. What is AI? • Not just making Computers, Robots, or agents acts like humans! – They should think like humans not like machines! – We don’t want them to make humans mistakes! – We want them to learn but not from the time 0! • The key is the ability to “share” learned results (i.e. copy data/program) between computers.
  • 5. So… • AI is not just studying intelligent systems, but building them… • Psychological approach: an intelligent system is a model of human intelligence! • Engineering approach: an intelligent system solves a sufficiently difficult problem in a generalizable way!
  • 6. The AI Semester Objectives • Become familiar with AI techniques, including their implementations – be able to develop AI applications using Python! • Understand the theory behind the techniques, knowing which techniques to apply when (and why) • Become familiar with a range of applications of AI – We will focus on Agent-based Modelling and applying it using NetLogo software!
  • 7. AI History? • Gestation (the early 1950’s): – McCulloch and Pitts artificial neuron, Hebbian learning – Early learning theory, first neural network, Turing test • Birth (1957): – The Logic Theorist – Name coined by McCarthy – Workshop at Dartmouth
  • 8. Cont’d… • Early enthusiasm, great expectations (1952- 1969) – GPS, physical symbol system hypothesis – Geometry Theorem Prover (Gelertner), Checkers (Samuels) – Lisp (McCarthy), Theorem Proving (McCarthy), Microworlds (Minsky et. al.) – “neat” (McCarthy @ Stanford) vs. “scruffy” (Minsky @ MIT)
  • 9. Cont’d… • Dose of Reality (1966-1973) – Combinatorial explosion • Knowledge-based systems (1969-1979) • AI Becomes an Industry (1980-present) – Boom period 1980-88, then AI Winter • Return of Neural Networks (1986-present) • AI Becomes a Science (1987-present) – SOAR, Internet as a domain
  • 10. What is AI (Again)? • Systems that think like • Systems that think humans! rationally! • Cognitive Modeling Approach – Laws of Thought approach • The automation of activities – The study of mental faculties that we associate with human through the use of computational thinking... models. • Systems that act like • Systems that act rationally! humans! • Rational Agent Approach • Turing Test Approach • The branch of CS that is • The art of creating machines concerned with the automation of that perform functions that intelligent behavior require intelligence when performed by people
  • 11. Acting Humanly! • Turning Machine: Introducing the concept of his universal abstract machine. – Simple and could solve any mathematical problem. • Turning test: if the machine could fool a human into thinking that it was also human, then it passed the intelligence test. Can Machines Think?
  • 12. Acting Humanly, Cont’d… • Operational test for intelligent behavior • The Imitation Game • Problem! – The turning test is not reproducible, constructive, or amenable to mathematical analysis
  • 13. Thinking Humanly! • 1960’s cognitive revolution • Requires scientific theories of internal activities of the brain • What level of abstraction? “Knowledge” or “Circuits” • How to validate? – Predicting and testing behavior of human subjects (top-down) – Direct identification from neurological data (bottom-up) • Cognitive Science and Cognitive Neuroscience • Now distinct from AI
  • 14. Thinking Rationally • Normative (or prescriptive) rather than descriptive • Aristotle: What are correct arguments / thought processes? • Logic notation and rules for derivation for thoughts • Problems: • Not all intelligent behavior is mediated by logical deliberation • What is the purpose of thinking? What thoughts should I have?
  • 15. Acting Rationally • Rational behavior • Doing the right thing • What is the “right thing”? • That which is expected to maximize goal achievement, given available information • We do many (“right”) things without thinking • Thinking should be in the service of rational action
  • 16. Applied Areas of AI • Heuristic Search • Computer Vision • Adversarial Search (Games) • Fuzzy Logic • Natural Language Processing • Knowledge Representation • Planning • Learning
  • 17. Concepts to be learned • Problem-Solving – Uninformed Search – Informed Search • AI and Games • Machine Learning • Evolutionary Computation • Robotics and AI • Intelligent Agents and Agent-based Modeling
  • 18. Semester Tools • References as Textbooks: – Artificial Intelligence a system approach, by M. Tim Jones, 2008. – Artificial Intelligence A modern Approach, 3rd Edition, by Stuart J. Russell and Peter Norving, 2010.