1. CSE 573 Artificial Intelligence Dan Weld Xu Miao www.cs.washington.edu/education/courses/cse573/04au

2. © Daniel S. Weld 2 Logistics: Dan Weld weld@cs.washington.edu Xu Miao xm@cs.washington.edu Required Reading Russell & Norvig “AIMA2” Papers from WWW Grading: Class Discussion Mini Projects Reviews on Reading Midterm & Problem Sets

3. © Daniel S. Weld 3 For You To Do Get on class mailing list Monitor class website for reading etc. Read Ch 1 [History] is interesting, but optional Ch 2 [Agents] is easy, but important Ch 3 [Search] is crucial, but should be review

4. © Daniel S. Weld 4 Goals of this Course To introduce you to a set of key: Paradigms & Techniques Teach you to identify when & how to use Heuristic search Constraint satisfaction Machine learning Logical inference Bayesian inference Policy construction Teach you how to evaluate (AI) papers Highlight directions for research

5. © Daniel S. Weld 5 Outline Logistics Objectives What is AI? State of the Art Challenges Agents

6. © Daniel S. Weld 6 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

7. © Daniel S. Weld 7 Hardware 10 11 neurons 10 14 synapses cycle time: 10 -3 sec 10 7 transistors 10 10 bits of RAM cycle time: 10 -9 sec

8. © Daniel S. Weld 8 Computer vs. Brain

9. © Daniel S. Weld 9 Evolution of Computers

10. © Daniel S. Weld 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.

11. © Daniel S. Weld 11 What is Intelligence?

12. © Daniel S. Weld 12 Dimensions of the AI Definition thought vs. behavior human-like vs. rational Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally

13. © Daniel S. Weld 13 AI as Science Where did the physical universe come from? And what laws guide its dynamics? How did biological life evolve? And how do living organisms function? What is the nature of intelligent thought?

14. © Daniel S. Weld 14 AI as Engineering How can we make software systems more powerful and easier to use? Speech & intelligent user interfaces Autonomic computing SPAM detection Mobile robots, softbots & immobots Data mining Modeling biological systems Medical expert systems...

15. © Daniel S. Weld 15 State of the Art 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 I could feel – I could smell – a new kind of intelligence across the table” -Gary Kasparov “

16. © Daniel S. Weld 16 Mathematical Calculation

17. © Daniel S. Weld 17 Shuttle Repair Scheduling

18. © Daniel S. Weld 18 courtesy JPL Started: January 1996 Launch: October 15th, 1998 Experiment: May 17-21

19. © Daniel S. Weld 19 Compiled into 2,000 variable SAT problem Real-time planning and diagnosis

20. © Daniel S. Weld 20 2004 & 2009

21. © Daniel S. Weld 21 Europa Mission ~ 2018

22. © Daniel S. Weld 22 Credit Card Fraud Detection

23. © Daniel S. Weld 23 Speech Recognition

24. © Daniel S. Weld 24 Autonomous Navigation: NAVLAB 1

25. © Daniel S. Weld 25 NAVLAB 2

26. © Daniel S. Weld 26 NAVLAB 11

27. © Daniel S. Weld 27 NAVLAB 5

28. © Daniel S. Weld 28 NAVLAB 7

29. © Daniel S. Weld 29 NAVLAB 23?

30. © Daniel S. Weld 30 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

31. © Daniel S. Weld 31 Role of Knowledge in Natural Language Understanding WWW Information Extraction Speech Recognition “word spotting” feasible today continuous speech – rapid progress Translation / Understanding limited progress The spirit is willing but the flesh is weak. (English) The vodka is good but the meat is rotten. (Russian)

32. © Daniel S. Weld 32 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.

33. © Daniel S. Weld 33 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 Learning Alternatives?

34. © Daniel S. Weld 34 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”

35. © Daniel S. Weld 35 Recurrent Themes II 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

36. © Daniel S. Weld 36 Recurrent Themes III 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?

37. © Daniel S. Weld 37 Recurrent Themes IV Importance of Representation Features in ML Reformulation

38. © Daniel S. Weld 38 573 Topics Agents Search thru Problem Spaces & Constraint Sat Knowledge Representation Learning Planning Markov Decision Processes Reinforcement Learning

39. © Daniel S. Weld Intelligent Agents Have sensors, effectors Implement mapping from percept sequence to actions Environment Agent percepts actions Performance Measure

40. © Daniel S. Weld Defn: Ideal rational agent Rationality vs omniscience? Acting in order to obtain valuable information “For each possible percept sequence, does whatever action is expected to maximize its performance measure on the basis of evidence perceived so far and built-in knowledge.''

41. © Daniel S. Weld Defn: Autonomy An agent is autonomous to the extent that its behavior is determined by its own experience Why is this important? The parable of the dung beetle

42. © Daniel S. Weld Implementing ideal rational agent Table lookup agents Agent program Simple reflex agents Agents with memory Reflex agent with internal state Goal-based agents Utility-based agents

43. © Daniel S. Weld Simple reflex agents ENVIRONMENT AGENT Effectors Sensors what world is like now Condition/Action rules what action should I do now?

44. © Daniel S. Weld Reflex agent with internal state ENVIRONMENT AGENTEffectors Sensors what world is like now Condition/Action rules what action should I do now? What world was like How world evolves

45. © Daniel S. Weld Goal-based agents ENVIRONMENT AGENT Effectors Sensors what world is like now Goals what action should I do now? What world was like How world evolves what it’ll be like if I do acts A 1 -A n What my actions do

46. © Daniel S. Weld Utility-based agents ENVIRONMENT AGENT Effectors Sensors what world is like now Utility function what action should I do now? What world was like How world evolves What my actions do How happy would I be? what it’ll be like if I do acts A 1 -A n

47. © Daniel S. Weld Properties of Environments Observability: full vs. partial vs. non Deterministic vs. stochastic Episodic vs. sequential Static vs. … vs. dynamic Discrete vs. continuous Travel agent WWW shopping agent Coffee delivery mobile robot