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CSC 110 – Fluency in Information Technology Chess

Explore the evolution of computer chess, from The Turk to modern algorithms like Minimax. Discover the complexities, challenges, and future prospects of artificial intelligence in strategic gameplay. Delve into the fascinating world of NLP and futuristic technologies in today's IT landscape. Engage in insightful discussions and labs to enhance your understanding in CSC.

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CSC 110 – Fluency in Information Technology Chess

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  1. CSC 110 – Fluency in Information TechnologyChess Dr. Curry Guinn

  2. Today’s Class • A Look Ahead • Computer Chess • Lab

  3. What’s Next? November 21 Fri Natural Language Processing November 24 Mon Futuristic Stuff November 26 Wed No Class November 28 Fri No Class December 1 Mon Review December 3 Wed Exam #2 December 10 Wed Final Exam Due

  4. Computer Chess • Chess playing machines have been around longer than computers • The Turk

  5. The Turk • 1769 • Complicated gear works • Could beat very, very good players • It was a hoax. • The Turk: The Life and Times of the Famous Eighteenth-Century Chess-Playing Machine

  6. Computer Chess • Programs first developed in 1958 • In 1997, Gary Kasparov lost a six game match to Deep Blue (3.5-2.5). • The primary algorithm used is called MINIMAX. • How does it work?

  7. Minimax relies on an Evaluation function • The Evaluation function rates how good a particular configuration is • In chess, the evaluation of a board position might include: • Number and type of pieces remaining on the board • How mobile are your pieces? • Do you control the center of the board? • Is the king in a safe position?

  8. Minimax Algorithm • See what moves are possible for you • Then for each of those moves, see what your opponent can do • Then for each of those moves, see what you can do … • Then see what your opponent can do … • And so on. • When you stop looking at possible moves, evaluate those final board positions. • Determine which sequence of moves will guarantee you the highest score. • You will always choose the move that results in a higher score; • Your opponent will always choose the move that results in a lower score

  9. 1 -3 C B D E F G 4 1 2 -3 4 -5 -5 1 -7 2 -3 -8 MAX 1 A MIN MAX = terminal position = agent = opponent

  10. Problems with Minimax • How far can you search in the tree? • In chess, between 25-40 moves possible on average. • So at each level of the tree, you could have 25 branches. • After 1 move, 25 possible boards. • After 2 moves, 252 = 625 • After 3 moves, 253 = 15, 625

  11. Depth of Minimax • After 4 moves, 254 = 390, 625 • After 5 moves, 255 = 9,765,625 • … • After 10 moves, 2510 = 95, 367, 431, 640, 625 • Searching to depth = 40, at one game per microsecond it would take 1090 years to make its first move • Age of universe = 1010

  12. Depth of Minimax (Deep Blue) • 480 specialty chess chips • Parallel computing: 30 workstations • Searches 12 plies (turns) • 25^12 = 59 quadrillion • Extends promising lines as must as 40 plies • PLUS opening database • PLUS end-game algorithms

  13. So Is It Intelligent?

  14. Games where MiniMax doesn’t work too well • Go • Branching factor of 200 • Games can go on for 250+ moves • Very difficult to evaluate board positions • This is the HOT game to study in AI • Card games • Too much variability due to chance

  15. Lab: Part 1 • Here’s another art program. It’s pretty good. • http://www.kurzweilcyberart.com/

  16. Lab: Play Chinook • Chinook is the best checkers player in the world • Try it: http://games.cs.ualberta.ca/~chinook/cgi-bin/player.cgi?nodemo

  17. Some great AI demos and movies • Demos: http://pages.cs.wisc.edu/~dyer/cs540/demos.html • A page with some of the best computer game players: http://www.cs.ualberta.ca/%7Egames/

  18. Wrap-up • Why is Natural Language Processing so hard? • Read this paper: • http://people.uncw.edu/guinnc/papers/ittsec_nlp.97.pdf

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