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CSE 573 Artificial Intelligence Dan Weld Autumn 2003 www.cs.washington.edu/education/courses/cse573/03au Logistics: • Dan Weld [email protected] • Masaharu Kobashi [email protected] • Required Reading Russell & Norvig “AIMA2” Papers from WWW • Grading: Class Discussion Problem Sets Project Reviews on Reading Final © Daniel S. Weld 2 • • • • • CSE 573 Artificial Intelligence Introduction Logistics Concept of an Agent Notion of a Problem Space Search Techniques © Daniel S. Weld 3 Goals of this Course • To introduce you to a set of key: paradigms, techniques and algorithms • Teach you how to evaluate (AI) papers • Highlight directions for research © Daniel S. Weld 4 What is Intelligence? © Daniel S. Weld 5 Hardware 1011 neurons 1014 synapses cycle time: 10-3 sec 107 transistors 1010 bits of RAM cycle time: 10-9 sec © Daniel S. Weld 6 Computer vs. Brain © Daniel S. Weld 7 Evolution of Computers © Daniel S. Weld 8 Projection •In near future computers will have As many processing elements as our brain, But far fewer interconnections Much faster updates. •Fundamentally different hardware Requires fundamentally different algorithms! Very much an open question. © Daniel S. Weld Dimensions of the AI Definition human-like vs. rational Systems that Systems that think like humans think rationally thought vs. behavior Systems that act Systems that act like humans rationally © Daniel S. Weld 10 Frontiers of AI “I could feel – I could smell – a new kind of intelligence across the table” -Gary Kasparov © Daniel S. Weld Saying Deep Blue doesn’t really think about chess is like saying an airplane doesn’t really fly because it doesn’t flap its wings. – Drew McDermott 11 Started: January 1996 Launch: October 15th, 1998 Experiment: May 17-21 © Daniel S. Weld courtesy JPL 12 Compiled into 2,000 variable SAT problem Real-time planning and diagnosis © Daniel S. Weld 13 Budgets Autonomy © Daniel S. Weld 14 Limits of AI Today • Today’s successful AI systems operate in well-defined domains employ narrow, specialize knowledge • Commonsense Knowledge needed in complex, open-ended worlds • Your kitchen vs. GM factory floor understand unconstrained Natural Language © Daniel S. Weld 15 Role of Knowledge in Natural Language Understanding • Speech Recognition “word spotting” feasible today continuous speech – rapid progress • Translation / Understanding very limited progress The spirit is willing but the flesh is weak. (English) The vodka is good but the meat is rotten. (Russian) © Daniel S. Weld 16 How the heck do we understand? • John gave Pete a book. • John gave Pete a hard time. • John gave Pete a black eye. • John gave in. • John gave up. • John’s legs gave out beneath him. • It is 300 miles, give or take 10. © Daniel S. Weld 17 How to Get Commonsense? • CYC Project (Doug Lenat, Cycorp) Encoding 1,000,000 commonsense facts about the world by hand Coverage still too spotty for use! (But see Digital Aristotle project) • Machine Leraning • Alternatives? © Daniel S. Weld 18 Historical Perspective • (4th C BC+) Aristotle, George Boole, Gottlob Frege, Alfred Tarski formalizing the laws of human thought • (16th C+) Gerolamo Cardano, Pierre Femat, James Bernoulli, Thomas Bayes formalizing probabilistic reasoning • (1950+) Alan Turing, John von Neumann, Claude Shannon thinking as computation • (1956) John McCarthy, Marvin Minsky, Herbert Simon, Allen Newell start of the field of AI © Daniel S. Weld 19 AI as Science Origin & Laws of the Physical Universe Origin & Laws of Biological Life Nature of Intelligent Thought © Daniel S. Weld 20 AI as Engineering • • • • Softbots & Intelligent User Interfaces Mobile Robots … Immobots Machine Learning Algorithms; Data Mining Medical Expert Systems... © Daniel S. Weld 21 AI Theory • For example, Machine Learning The concept orgespat? • Given tree structured instance space with n attributes, you need 4 log(2/d) + 16n log(13/e))/e examples to learn with high probability (1-d) a concept which is approximately (1-e) correct © Daniel S. Weld 22 Recurrent Themes • Representation vs. Implicit Neural Nets - McCulloch & Pitts 1943 • Died out in 1960’s, revived in 1980’s • Simplified model of real neurons, but still useful; parallelism Brooks “Intelligence without Reprsentation” • Logic vs. Probability In 1950’s, logic dominates (McCarthy, … • attempts to extend logic “just a little” (e.g. nomon) 1988 – Bayesian networks (Pearl) • efficient computational framework Today’s hot topic: combining probability & FOL © Daniel S. Weld 23 Recurrent Themes II • Weak vs. Strong Methods • Weak – general search methods (e.g. A* search) • Knowledge intensive (e.g expert systems) • more knowledge less computation • Today: resurgence of weak methods • desktop supercomputers • How to combine weak & strong? • Importance of Representation • Features in ML • Reformulation © Daniel S. Weld 24 573 Topics • • • • • • • Agents Search thru Problem Spaces Constraint Satisfaction Knowledge Representation Planning Markov Decision Processes Reinforcement Learning © Daniel S. Weld 25