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CS 440 / ECE 448 Introduction to Artificial Intelligence Fall 2006

CS 440 / ECE 448 Introduction to Artificial Intelligence Fall 2006. Instructor: Eyal Amir TAs: Deepak Ramachandran (head TA), Jaesik Choi. Today. Artificial Intelligence Motivation – the dream Long-term goals Short-term applications What you will learn Tools, concepts, thought

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CS 440 / ECE 448 Introduction to Artificial Intelligence Fall 2006

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  1. CS 440 / ECE 448Introduction to Artificial IntelligenceFall 2006 Instructor: Eyal Amir TAs: Deepak Ramachandran (head TA), Jaesik Choi

  2. Today • Artificial Intelligence • Motivation – the dream • Long-term goals • Short-term applications • What you will learn • Tools, concepts, thought • What you should know • Administration of this class

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

  4. Example: Shakey (1971)

  5. Example: Mobile Robots (2001)

  6. Example: (2005) DARPA Grand Challenge

  7. Example: (2005) DARPA Grand Challenge

  8. 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

  9. 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

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

  11. What This Course Covers • Major techniques used in artificial intelligence • Vision • Probabilistic reasoning • Learning • Knowledge representation - logic & probability • Logical reasoning • Robotic control, Stimulus-Response • Planning and sequential decision making

  12. Today’s Handouts • Syllabus and Dates • Readings • Midterms, Final exam • Home assignments • Project assignments • Class website: http://reason.cs.uiuc.edu/cs440

  13. Project: Autonomous Car • Project divided into milestones: • Vision • Tracking • Reactive control • High-level control • Combination • Teams of 4-6 people – send team composition to TAs • After the end of semester... Project continues until the DARPA Urban Competition (Dec 2007).

  14. What you should know • Matrix Algebra • Probability and Statistics • Logic • Data structures • C++

  15. What You Will Know • Matlab • LISP / Prolog • 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

  16. Administration of This Class • Homeworks every week • see homework #0 given today • Handouts given in class • Homeworks collected: beginning of class • Homeworks returned within a week • 7 days flexible grace period – once spent, 20% off for every late day

  17. More Administration • Cheating policy: • 0 on first occurrence, F on second • Honor code • Grading formula: • 3hr credit = Mid1(20%)+Mid2(20%)+Final(20%)+HW(20%)+Proj(20%) • 4hr credit = Mid1(15%)+Mid2(15%)+Final(15%)+HW(15%)+Proj(40%) • Extra credit for class participation (3%) • Project grading • Grade given to group every milestone (5 milestones total) • Adjusted grade to individual contribution • Final exam only on last 1/3 material

  18. Office Hours • Eyal: Thu 4pm-5pm – SC 3314 • TAs (Deepak, Jaesik): Tue 3pm-5pm – SC 0207 • Communication: • Newsgroup: class.cs440 on nntp.cs.uiuc.edu • Special requests: ta440@cs.uiuc.edu

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