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CS 440 / ECE 448 Introduction to Artificial Intelligence Spring 2010

CS 440 / ECE 448 Introduction to Artificial Intelligence Spring 2010. Instructor: Eyal Amir Grad TAs : Wen Pu, Yonatan Bisk Undergrad TAs : Sam Johnson, Nikhil Johri. Artificial Intelligence (AI). Natural Language. Vision. Reasoning. Knowledge. Decision Making. Learning. Robotics.

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CS 440 / ECE 448 Introduction to Artificial Intelligence Spring 2010

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  1. CS 440 / ECE 448Introduction to Artificial IntelligenceSpring 2010 Instructor: Eyal Amir Grad TAs: Wen Pu, Yonatan Bisk Undergrad TAs: Sam Johnson, Nikhil Johri

  2. Artificial Intelligence (AI) Natural Language Vision Reasoning Knowledge Decision Making Learning Robotics

  3. Natural Language Econometrics Vision Databases Reasoning Medicin Knowledge Networks Decision Making Autonomous Vehicles Learning Social Science Robotics Electronic Commerce AI Applications

  4. Today • Artificial Intelligence Applications • Artificial Intelligence Basics • What you think you know • Logic • Probabilities • AI • Search

  5. What is Artificial Intelligence? • Examples: • Game playing? (chess) • Robots? (Roomba) • Learning? (Amazon) • Autonomous space crafts? (NASA) • What should AI have?

  6. Saw in Yonatan’s Presentation • Robotics • Vision • A little bit of Natural-Language Processing

  7. Game Playing: Chess May 1997 2006: Anthony Cozzie’s (UIUC) ZAPPA wins World Computer-Chess Championship

  8. Decision Making: Scrabble Daily Illini Feb 2007: Winning computer program created by graduate student beats world champion Scrabble player (Graduate Student = Mark Richards)

  9. Collaborative Filtering

  10. Classification

  11. Planning

  12. DARPA Grand Challenge 2003-2007

  13. Econometrics Example: A Recession Model of a country • What is probability of recession, when a bank(bm) goes into bankruptcy? • Recession: Recession of a country in [0,1] • Market[X]: Quarterly market (X) index • Loss[X,Y]: Loss of a bank (Y) in a market (X) • Revenue[Y]: Revenue of a bank (Y)

  14. Social Networks Example: school friendships and their effects Friend(A,B) Attr(A) Measuremt(A) shorthand for Friend(., .), Atrr(.), and Measuremt(.) potential func­tions Friend(A,C) Attr(B) Measuremt(B) Friend(B,C) Attr(C) Measuremt(C)

  15. hlia blia hjoe htom hbob btom hann bjoe bann bbob hval bval f f f f f f f f f f f f f f f f f f f f f f f f f f f f f f tom; val bob; lia lia; tom ann; lia ann; tom lia; ann val; bob tom; ann joe; val ann; joe val; joe tom; lia bob; val lia; joe bob; tom val; tom joe; ann ann; bob val; lia joe; bob tom; bob joe; tom tom; joe joe; lia bob; ann lia; val val; ann ann; val lia; bob bob; joe

  16. Application: Hardware Verification f3 x1 f1 not AND x2 f5 AND not f2 OR x3 f4 Question: Can we set this boolean cirtuit to TRUE? f5(x1,x2,x3) = a function of the input signal

  17. Application: Hardware Verification f3 x1 f1 not AND x2 f5 AND not f2 OR SAT(f5) ? x3 f4 Question: Can we set this boolean cirtuit to TRUE? f5(x1,x2,x3) = f3 f4 = f1  (f2  x3) = (x1  x2)  (x2  x3) M[x1]=FALSE M[x2]=FALSE M[x3]=FALSE

  18. Finding the “best” path between two points • Classic computer science problem: many algorithms, applications • “best” generally means minimizing some sort of cost each edge has some cost associated with it cost of path generally sum etc. of cost of edges along path 10 10 10 source s 10 sink t

  19. Stochastic setting • Edges fail probabilistically • Goal: find most reliable path Directed Acyclic Graph G edge reliability t 0.85 s 0.9 0.95 path reliability = 0.95 x 0.9 x 0.85 = 0.73 assumption: independent!!! not very realistic...

  20. Stochastic setting • Consider a richer structure using a graphical model (discrete) hidden variable X t e3 s e2 e1 the hidden variable allows us to model correlations and dependencies between edge failures binary random variables: 1 if edge survives, 0 if edge fails

  21. Stochastic setting • Specified: • prior probability on X • conditional probabilities for each edge Pr[X=1] = 0.4 Pr[X=2] = 0.1 Pr[X=3] = 0.2 Pr[X=4] = 0.3 Pr[e1 survives | X=1] = 0.9 Pr[e1 fails | X=1] = 0.1 ... etc. X t e3 s e2 e1

  22. Stochastic setting • Graphical model defines joint distribution: Pr[X,e1,e2,e3,...] = Pr[X] Pr[e1|X] Pr[e2|X]... • Reliability of path is marginal Pr[e1,e2,e3] • Can compute by summing... X t e3 s e2 e1

  23. Many applications • Just to name a few: • Network QoS routing[citations] routers fail stochastically links fail stochastically Failures are typically correlated: if two machines run the same version of unpatched Windows, and one gets infected by a virus...

  24. Many applications • Just to name a few: • Network QoS routing [citations] • Parsing w/ weighted FSAs FSA where edges have probabilities assigned to them (from Smith + Eisner ACL’05 best paper)

  25. Many applications • Just to name a few: • Network QoS routing • Parsing w/ weighted FSAs • Robot navigation e.g., DARPA Grand Challenge

  26. Motivation of AI • Autonomous computers • Embedded computers • Programming by telling • Human-like capabilities – vision, natural language, motion and manipulation • Applications: learning, media, www, manipulation, verification, robots, cars, help for disabled, dangerous tasks

  27. Long-Term Goals • Computers that can accept advice • Programs that process rich information about the everyday world • Programs that can replace experts • Computer programs that can decide on actions: control, planning, experimentation • Programs that combine knowledge of different types and sources • Programs that learn

  28. Short-Term Goals • Knowledge & reasoning – acquire, represent, use, answer questions • Planning & decision making • Diagnosis & analysis • Learning, pattern recognition • Inferring state of the world from sensors • Vision • Natural-language text

  29. What This Course Covers • Major techniques in artificial intelligence • Search in large spaces and game search • Logical reasoning • Planning and sequential decision making • Knowledge representation - logic & probability • Probabilistic reasoning • Machine Learning • Robotic control, Stimulus-Response • Machine Vision

  30. What you should know • Matrix Algebra • Probability and Statistics • Logic • Data structures • C++, Java, Python, or Matlab

  31. What You Will Know • Matlab • Building and reasoning with complex probabilistic and logical knowledge • Build autonomous agents • Create vision/sensing routines for simple detection, identification, and tracking • Create programs that make decisions autonomously or semi-autonomously

  32. Administration • Office Hours, Late policy, homework deadlines, syllabus, and how to make a home-cooked meal – check the website: http://www.cs.uiuc.edu/class/cs440

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