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G5AIAI Introduction to Artificial Intelligence

G5AIAI Introduction to Artificial Intelligence. Course Introduction. Graham Kendall. You are a caveman (or woman) I travel back in time and bring you a Pentium IV PC and show you some of the things it is capable of doing.

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G5AIAI Introduction to Artificial Intelligence

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  1. G5AIAIIntroduction to Artificial Intelligence Course Introduction Graham Kendall

  2. You are a caveman (or woman) • I travel back in time and bring you a Pentium IV PC and show you some of the things it is capable of doing. • Question : Would you, as a caveman, consider the computer to be intelligent? • Hands up if you think the computer is intelligent?

  3. Now we are in the present day • I show you the same Pentium III PC and show you some of the things it is capable of doing. • Question : Would you, as a computer science student, consider the computer to be intelligent? • Hands up if you now think the computer is intelligent?

  4. We are still in the present day, but we are at the end of this lecture course. • Just before I leave the final lecture, I peel away my face and reveal I am an android. As an android I have just delivered a 24 hour lecture course and answered all your questions. • Question : Would you, as a computer science student, consider me to be intelligent? • Hands up if you think I am intelligent?

  5. Course Introduction • Course • Introduction to Artificial Intelligence • Lecturer • Graham Kendall • GXK@CS.NOTT.AC.UK

  6. Course Introduction • Web Page • http://www.cs.nott.ac.uk/~gxk/courses/g5aiai/ • EMAIL • Second / Third Year Projects

  7. Course Context G5AIAI Introduction to AI G5BAIP Artificial Intelligence Programming G5BAIM Artificial Intelligence Methods D53DIA Designing Intelligent Agents

  8. Who Can Attend? (This year) • Introduction to AI • First/Second Year Option • Artificial Intelligence Methods • Second/Third Year Option

  9. Course Introduction • Last Years Course • Good Points • Handouts/Notes • Summary of Each Lecture • Willingness to Answer Questions • Bad Points • Speak Properly (Louder, Clearer, Faster, Slower) • Did Not Summarise Lectures • More Coursework Needed • Feedback on Coursework Needed • Course content was too much / too little

  10. Course Introduction • Lectures • 15 (ish) • Lecture Times and Locations • Monday 13:00 (CTF-LT2) • Friday 11:00 (CTF-LT3) • Assessment • 25% Coursework • 75% examination (2 hours)

  11. Aims of the Course • Define what we mean by AI (or at least give us a working definition for this course) • Write “AI” programs that • Explore search spaces using both blind and heuristic search techniques • Implement Neural Networks (perceptron)

  12. Aims of the Course • This is very much a practical course. Although we will touch upon the philosophical issues we will not dwell on this area of AI. • This course is more concerned with writing useful AI programs than discussing if a computer is intelligent or not.

  13. Textbooks • Artificial Intelligence – A Modern Approach (AIMA) (Russell/Norvig) Much of the material for this course is from this book. If you are going to buy a book, then get this one. (Web site has an amazon link)

  14. Textbooks • Artificial Intelligence (Rich/Knight) • This used to be the “standard” AI text book but AIMA is now taking its place – in my view

  15. Textbooks • Artificial Intelligence (Winston) • As for Rich/Knight

  16. Textbooks • Artificial intelligence : Structure and Strategies for Complex Problem Solving (Luger/Stubblefield)

  17. Textbooks • Computational Intelligence (Poole/Mackworth/Goebel) • As good as AIMA but came out later

  18. Useful Reading • The Essence of Artificial Intelligence (Cawsey)

  19. Useful Reading • Seven Methods for Transforming Corporate Data into Business Intelligence (Dahr/Stein)

  20. Useful Reading • Artificial Intelligence and Computer Games (Richard Bartle)

  21. Useful Reading • Computer Gamesmanship (David Levy)

  22. Neural Networks • The Essence of Neural Networks (Callan)

  23. Neural Networks • Neural Network (Davalo)

  24. Neural Networks • Fundamentals of Neural networks (Fausett)

  25. Proposed Lecture Schedule • See Web Site

  26. Coursework Schedule • Coursework • Is already available • It does involve programming • Closing date is 4th December, but with an automatic weeks extension! • No further extensions

  27. Examinations • Examination Rubric • You are expected to answer one of the two questions from section A and three of the five questions from section B.

  28. Are these AI?? Boids

  29. Course Introduction FinishedNow the hard work starts

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