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Lec1 introduction



a brief introduction to Artificial intelligence, idea and concepts of learning and teaching

a brief introduction to Artificial intelligence, idea and concepts of learning and teaching



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Lec1 introduction Lec1 introduction Presentation Transcript

  • Artificial IntelligenceLecturere: Sheheen A. AbdulkareemDept. of Computer ScienceFaculty of ScienceUniversity of DohukSep, 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 ones 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. View slide
  • Hmm! View slide
  • 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.