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Introduction to Introduction to Artificial Intelligence

Introduction to Introduction to Artificial Intelligence

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Introduction to Introduction to Artificial Intelligence

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  1. Introduction to Introduction to Artificial Intelligence Henry Kautz

  2. Introductions • Henry Kautz • Office hours: Tuesday 1:30-2:30, Thursday 10:30-11:30, Room 666 • Daniel Lowd • Office hours: Wednesday 3:00-4:00,Room 430 • Text: Russell & Norvig, AI: A Modern Approach, 2nd Edition • Sign up for class email list

  3. Coursework • Weekly written and short programming assignments • Posted on web page only • Read assigned chapters before class on that topic! • Significant final project & write up • No exams • Collaboration policy: • Good to talk to other students about assignments • Write up your own solution afterward • Cite other sources of information • students, web, papers

  4. Discrete Inference • State space search • Heuristics • Propositional logic • Local search • Constraint satisfaction • Compiling to SAT • First-order logic • Logic programming

  5. Probabilistic Inference • Probability theory • Bayesian networks • Undirected graphical models • Dynamic probabilistic models • Decision theory • Markov decision processes

  6. Learning • Decision tree learning • Ensemble learning • Learning graphical models • Neural networks • Support vector machines

  7. Today • History of AI • State space search • DFS, BFS, IDFS • Best first • A* • STRIPS planning by state space search • Assignment

  8. Forerunners of AI • Logic: rules of rational thought • Aristotle (384-322 BC) – syllogisms • Boole (1815-1864) – propositional logic • Frege (1848-1925) – first-order logic • Hilbert (1962-1943) – “Hilbert’s Program” • Gödel (1906-1978) – incompleteness • Turing (1912-1954) – computability, Turing test • Cook (1971) – NP completeness

  9. Forerunners of AI • Probability & Game Theory • Cardoano (1501-1576) – probabilities • Bernoulli (1654-1705) – random variables • Bayes (1702-1761) – belief update • von Neumann (1944) – game theory • Richard Bellman (1957) – MDP

  10. Early AI • Neural networks • McCulloch & Pitts (1943) • Rosenblatt (1962) – perceptron learning • Symbolic processing • Dartmouth conference (1956) • Newell & Simon – logic theorist • John McCarthy – symbolic knowledge representation • Samuel's Checkers Program

  11. Battle for the Soul of AI • Minsky & Papert (1969) – Perceptrons • Single-layer networks cannot learn XOR • Argued against neural nets in general • Backpropagation • Invented in 1969 and again in 1974 • Hardware too slow, until rediscovered in 1985 • Research funding for neural nets disappears • Rise of rule-based expert systems

  12. Knowledge is Power • Expert systems (1969-1980) • Dendral – molecular chemistry • Mycin – infectious disease • R1 – computer configuration • AI Boom (1975-1985) • LISP machines • Japan’s 5th Generation Project

  13. AI Winter • Expert systems oversold • Fragile • Hard to build, maintain • AI Winter (1985-1990) • Science went on... looking for • Principles for robust reasoning • Principles for learning

  14. Symbols + Numbers • Graphical probabilistic models • Pearl (1988) – Bayesian networks • Machine learning • Quinlan (1975) – ID3 (aka C4.5) • Vapnik (1992) – Support vector machines • Schapire (1996) – Boosting • Hot topic: statistical relational learning

  15. Success Stories • Deep Space One (1996) • Deep Blue (1997) • Countless AI systems in day to day use • Computational biology • Market research • Planning & scheduling • Hardware verification • Threat assessment

  16. State-Space Search

  17. Non-Optimality of Best-First Search Path found by Best-first 53nd St 52nd St S G 51st St 50th St 10th Ave 9th Ave 8th Ave 7th Ave 6th Ave 5th Ave 4th Ave 2nd Ave 3rd Ave Shortest Path

  18. Maze Runner

  19. Assignment