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CS 480 Lec 2 Sept 4

CS 480 Lec 2 Sept 4. complete the introduction Chapter 3 (search). The party example. If Alex goes, then Beki goes: A  B If Chris goes, then Alex goes: C  A Beki does not go: not B Chris goes: C

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CS 480 Lec 2 Sept 4

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  1. CS 480 Lec 2 Sept 4 complete the introduction Chapter 3 (search)

  2. The party example • If Alex goes, then Beki goes: A  B • If Chris goes, then Alex goes: C  A • Beki does not go: not B • Chris goes: C Query: Is it possible to satisfy all these conditions? This is called satisfiability problem.

  3. Example of languages • Programming languages: • Formal languages, not ambiguous, but cannot express partial information. Not expressive enough. • Natural languages: • Very expressive but ambiguous: ex: small dogs and cats. • Good representation language: • Both formal and can express partial information, can accommodate inference • Main approach used in AI: Logic-based languages. • Predicate-logic with Horn clauses

  4. Deduction algorithms Given P  R, and Q  ~R Can we deduce ~(P & Q)? Resolution strategy (Robinson) Example: • Applications • expert systems (Mycin, dendral are early examples) • logic programming • automatic theorem proving (software validation)

  5. Logical deduction in predicate logic Example: X (Y ((mother(X) child_of(Y,X)) loves(X,Y))) mother(mary) child_of(tom,mary) Can we deduce? loves(mary, tom) Resolution strategy of Robinson also works for predicate logic. But the time complexity is very high.

  6. techniques • Probabilistic approach to AI Knowledge representation models uncertainties. Example: • H = “Have a headache” • F = “Coming down with Flu” • P(H) = 1/10 • P(F) = 1/40 • P(H|F) = ½ Given that you have a headache, what is the probability that you have flu? This kind of modeling is widely used in various prediction problems, e.g., in determining the insurance premium for car etc.

  7. Probabilistic approach to AI • Some games are inherently probabilistic. • Financial markets • backgammon

  8. techniques Training set New applicant: (young, has job, does not own house, good credit). Will (s)he default? We can build a probabilistic model to answer.

  9. Classification model – decision tree, Naïve Bayes

  10. Bayesian network

  11. techniques Machine learning approach to AI: • self-improving algorithms • solution obtained without explicit programming • Closer to modeling human intelligence or natural intelligence (we learn many things by observing even if step by step procedure absent) Prominent examples: • Neural networks • Genetic algorithms, evolutionary method

  12. techniques Neuron (very roughly modeled by neurons in brain)

  13. What a single neuron can and can’t classify? Using 2 neurons, we can classify the right.

  14. techniques An algorithm called back propagation algorithm is used to adjust the weights of neurons based on the discrepancy between correct output and computed output.

  15. techniques • Evolutionary algorithms: • encoding of the collection of solutions as strings. • goal is to evolve the “best” solution. • use cross-over and mutation and iterate. Example of cross-over and mutation

  16. AI prehistory • Philosophy Logic, methods of reasoning, mind as physical system foundations of learning, language, rationality • Mathematics Formal representation and proof algorithms, computation, (un)decidability, (in)tractability, probability • Economics utility, decision theory • Neuroscience physical substrate for mental activity • Psychology phenomena of perception and motor control, experimental techniques • Computer building fast computers engineering • Control theory design systems that maximize an objective function over time • Linguistics knowledge representation, grammar

  17. Abridged history of AI • 1943 McCulloch & Pitts: Boolean circuit model of brain • 1950 Turing's "Computing Machinery and Intelligence" • 1956 Dartmouth meeting: "Artificial Intelligence" adopted • 1950s Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine • 1965 Robinson's complete algorithm for logical reasoning (resolution technique) • 1966—73 AI discovers computational complexity Neural network research almost disappears • 1969—79 Early development of knowledge-based systems • 1980-- AI becomes an industry • 1986-- Neural networks return to popularity • 1987-- probabilistic techniques dominate • 1995-- major advances in natural languages, web applications

  18. State of the art • Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997 • Proved a mathematical conjecture (Robbins conjecture) unsolved for decades • No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego) • During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people • NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft • Proverb solves crossword puzzles better than most humans

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