CS39N The Beauty and Joy of Computing Lecture #4 : Computational Game Theory 2009-09-14 A 19-year project led by Prof Jonathan Schaeffer, he used dozens.

Slides:



Advertisements
Similar presentations
CMSC 471 Spring 2014 Class #9 Tue 2/25/14 Game Playing Professor Marie desJardins,
Advertisements

Game Playing CS 63 Chapter 6
Games & Adversarial Search Chapter 5. Games vs. search problems "Unpredictable" opponent  specifying a move for every possible opponent’s reply. Time.
An Introduction to... Evolutionary Game Theory
AI for Connect-4 (or other 2-player games) Minds and Machines.
1 CSC 550: Introduction to Artificial Intelligence Fall 2008 search in game playing  zero-sum games  game trees, minimax principle  alpha-beta pruning.
CS 484 – Artificial Intelligence
CS10 The Beauty and Joy of Computing Lecture #22 : Computational Game Theory A 19-year project led by Prof Jonathan Schaeffer, he used dozens.
Heidi Newton Peter Andreae Artificial Intelligence.
"Programming a Computer to Play Chess" A presentation of C. Shannon's 1950 paper by Andrew Oldag April
Artificial Intelligence AI: The branch of computer science dedicated to the creation of machines that perform task which if done by humans would be considered.
G5AIAI Introduction to AI Graham Kendall Game Playing Garry Kasparov and Deep Blue. © 1997, GM Gabriel Schwartzman's Chess Camera, courtesy IBM.
Games CPSC 386 Artificial Intelligence Ellen Walker Hiram College.
Computers Playing Games Arif Zaman CS 101. Acknowledgements Portions of this are taken from MIT’s open-courseware
Mastering Chess An overview of common chess AI Adam Veres.
CS10 : The Beauty and Joy of Computing Lecture #14 : Computational Game Theory A 19-year project led by Prof Jonathan Schaeffer, he used dozens.
Everything You Always Wanted To Know about Game Theory* *but were afraid to ask Dan Garcia, UC Berkeley David Ginat, Tel-Aviv University Peter Henderson,
CS10 The Beauty and Joy of Computing Lecture #22 : Computational Game Theory A 19-year project led by Prof Jonathan Schaeffer, he used dozens.
UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering CSCE 580 Artificial Intelligence Ch.6: Adversarial Search Fall 2008 Marco Valtorta.
CHECKMATE! A Brief Introduction to Game Theory Dan Garcia UC Berkeley The World Kasparov.
Computers Playing Games Arif Zaman CS 101. Acknowledgements Portions of this are taken from MIT’s open-courseware
CHECKMATE! A Brief Introduction to Game Theory Dan Garcia UC Berkeley The World Kasparov.
CS70 L23 Hashing (1)Dan Garcia © UCB Dan Garcia ( inst.eecs.berkeley.edu/~cs70/ 1 Handout: notes Computer Science.
How computers play games with you CS161, Spring ‘03 Nathan Sturtevant.
CS10 The Beauty and Joy of Computing Lecture #25 : Tree Recursion The newly released (and much- hyped) Microsoft Kinect system for the XBOX.
CS10 The Beauty and Joy of Computing Lecture #23 : Limits of Computing Thanks to the success of the Kinect, researchers all over the world believe.
Computer Chess. Humble beginnings The Turk Ajeeb Mephisto El Ajedrecista.
Game Playing: Adversarial Search Chapter 6. Why study games Fun Clear criteria for success Interesting, hard problems which require minimal “initial structure”
Games. The Turk Built by Wolfgang von Kempelen (1734 – 1804).
Dr Rong Qu Module Introduction.
Game Playing State-of-the-Art  Checkers: Chinook ended 40-year-reign of human world champion Marion Tinsley in Used an endgame database defining.
Student view of SE study program at FER, Zagreb Ivan Belfinger Mentor: prof. dr. sc. Krešimir Fertalj Faculty of Electrical Engineering and Computing,
Introduction to AI, H. Feili 1 Introduction to Artificial Intelligence LECTURE 1: Introduction What is AI? Foundations of AI The.
Game Trees: MiniMax strategy, Tree Evaluation, Pruning, Utility evaluation Adapted from slides of Yoonsuck Choe.
PSU CS 370 – Introduction to Artificial Intelligence Game MinMax Alpha-Beta.
Minimax Trees: Utility Evaluation, Tree Evaluation, Pruning CPSC 315 – Programming Studio Spring 2008 Project 2, Lecture 2 Adapted from slides of Yoonsuck.
Artificial Intelligence (AI) Can Machines Think?.
Game Playing.
Do you drive? Have you thought about how the route plan is created for you in the GPS system? How would you implement a cross-and- nought computer program?
Projects Analyze Existing Game project –Due next class. Re-check the guidelines on course’s website! Create your Own Project –Due: Final game due Tuesday.
Mark Dunlop, Computer and Information Sciences, Strathclyde University 1 Algorithms & Complexity 5 Games Mark D Dunlop.
150 Students Can’t Be Wrong! GamesCrafters, a Computational Game Theory Undergraduate Research and Development Group at UC Berkeley 12:00-13:00.
Game-playing AIs Part 1 CIS 391 Fall CSE Intro to AI 2 Games: Outline of Unit Part I (this set of slides)  Motivation  Game Trees  Evaluation.
Game Playing. Towards Intelligence? Many researchers attacked “intelligent behavior” by looking to strategy games involving deep thought. Many researchers.
Application of Artificial Intelligence to Chess Playing Jason Cook Capstone Project.
The role of data mining in the last presidential election. A mind-blowing piece on how the Obama campaign used various sources of data to target voters.
Games. Adversaries Consider the process of reasoning when an adversary is trying to defeat our efforts In game playing situations one searches down the.
Introduction to Machine Learning Kamal Aboul-Hosn Cornell University Chess, Chinese Rooms, and Learning.
Beauty and Joy of Computing Limits of Computing Ivona Bezáková CS10: UC Berkeley, April 14, 2014 (Slides inspired by Dan Garcia’s slides.)
Today’s Topics Playing Deterministic (no Dice, etc) Games –Mini-max –  -  pruning –ML and games? 1997: Computer Chess Player (IBM’s Deep Blue) Beat Human.
The Beauty and Joy of Computing Lecture #6 Algorithms I UC Berkeley EECS Sr Lecturer SOE Dan Garcia.
An Introduction to Game Theory Math 480: Mathematics Seminar Dr. Sylvester.
CSE373: Data Structures & Algorithms Lecture 23: Intro to Artificial Intelligence and Game Theory Based on slides adapted Luke Zettlemoyer, Dan Klein,
Easy, Hard, and Impossible Elaine Rich. Easy Tic Tac Toe.
The Beauty and Joy of Computing Lecture #23 Limits of Computing Researchers at Facebook and the University of Milan found that the avg # of “friends” separating.
M9302 Mathematical Models in Economics Instructor: Georgi Burlakov 0.Game Theory – Brief Introduction Lecture
Adversarial Search and Game Playing Russell and Norvig: Chapter 6 Slides adapted from: robotics.stanford.edu/~latombe/cs121/2004/home.htm Prof: Dekang.
UC Berkeley EECS Sr Lecturer SOE Dan Garcia printing-aims-to-prevent-a-piracy-plague/ Quest.
Lec 23 Chapter 28 Game Theory.
The Beauty and Joy of Computing Lecture #16 Computational Game Theory A 19-year project led by Prof Jonathan Schaeffer, he used dozens (sometimes hundreds)
Explorations in Artificial Intelligence Prof. Carla P. Gomes Module 5 Adversarial Search (Thanks Meinolf Sellman!)
Luca Weibel Honors Track: Competitive Programming & Problem Solving Partisan game theory.
PENGANTAR INTELIJENSIA BUATAN (64A614)
CS Fall 2016 (Shavlik©), Lecture 11, Week 6
David Kauchak CS52 – Spring 2016
Artificial Intelligence and Searching
Breakfast Bytes Easy, Hard, and Impossible
M9302 Mathematical Models in Economics
Artificial Intelligence and Searching
Presentation transcript:

CS39N The Beauty and Joy of Computing Lecture #4 : Computational Game Theory A 19-year project led by Prof Jonathan Schaeffer, he used dozens (sometimes hundreds) of computers and AI to prove it is, in perfect play, a … draw! This means that if two Gods were to play, nobody would ever win! UC Berkeley Computer Science Lecturer SOE Dan Garcia

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (2) Garcia, Fall 2009  History  Definitions  Game Theory  What Games We Mean  Win, Lose, Tie, Draw  Weakly / Strongly Solving  Gamesman  Dan’s Undergraduate R&D Group  Demo!!  Future Computational Game Theory

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (3) Garcia, Fall 2009  CS research areas:  Artificial Intelligence  Biosystems & Computational Biology  Computer Architecture & Engineering  Database Management Systems  Graphics  Human-Computer Interaction  Operating Systems & Networking  Programming Systems  Scientific Computing  Security  Theory  … Computer Science … A UCB viewwww.eecs.berkeley.edu/Research/Areas/

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (4) Garcia, Fall 2009  A Hoax!  Built by Wolfgang von Kempelen  to impress the Empress  Could play a strong game of Chess  Thanks to Master inside  Toured Europe  Defeated Benjamin Franklin & Napoleon!  Burned in an 1854 fire  Chessboard saved… The Turk (1770) The Mechanical Turk (1770) en.wikipedia.org/wiki/The_Turk

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (5) Garcia, Fall 2009  The “Father of Information Theory”  Founded the digital computer  Defined fundamental limits on compressing/storing data  Wrote “Programming a Computer for Playing Chess” paper in 1950  C. Shannon, Philos. Mag. 41, 256 (1950).  All chess programs today have his theories at their core Claude Shannon’s Paper (1950)en.wikipedia.org/wiki/Claude_Shannon#Shannon.27s_computer_chess_program Claude Shannon ( )

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (6) Garcia, Fall 2009  Kasparov World Champ  1996 Tournament  First game DB wins a classic!  But DB loses 3 and draws 2 to lose the 6-game match 4-2  In 1997 Deep Blue upgraded, renamed “Deeper Blue”  1997 Tournament  GK wins game 1  GK resigns game 2  even though it was draw!  DB & GK draw games 3-5  Game 6 : (May 11 th )  Kasparov blunders move 7, loses in 19 moves. Loses tournament 3 ½ - 2 ½  GK accuses DB of cheating. No rematch.  Defining moment in AI history Deep Blue vs Garry Kasparov (1997)en.wikipedia.org/wiki/Deep_Blue_(chess_computer) IBM’s Deep Blue vs Garry Kasparov

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (7) Garcia, Fall 2009 Economic  von Neumann and Morgenstern’s 1944 Theory of Games and Economic Behavior  Matrix games  Prisoner’s dilemma, auctions  Film : A Beautiful Mind (about John Nash)  Incomplete info, simultaneous moves  Goal: Maximize payoff Computational  R. C. Bell’s 1988 Board and Table Games from many Civilizations  Board games  Tic-Tac-Toe, Chess, Connect 4, Othello  Film : Searching for Bobby Fischer  Complete info, alternating moves  Goal: VariesCombinatorial Sprague and Grundy’s 1939 Mathematics and Games Board games Nim, Domineering, dots and boxes Film: Last Year in Marienbad Complete info, alternating moves Goal: Last movewww.cs.berkeley.edu/~ddgarcia/eyawtkagtbwata What is “Game Theory”?

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (8) Garcia, Fall 2009  No chance, such as dice or shuffled cards  Both players have complete information  No hidden information, as in Stratego & Magic  Two players (Left & Right) usually alternate moves  Repeat & skip moves ok  Simultaneous moves not ok  The game can end in a pattern, capture, by the absence of moves, or … What “Board Games” do you mean?

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (9) Garcia, Fall 2009 Basic Definitions  Games are graphs  Position are nodes  Moves are edges  We strongly solve game by visiting every position  “Playing” every game ever  Each position is (for player whose turn it is) Winning (  losing child) Losing (All children winning) Tieing (!  losing child, but  tieing child) Drawing (can’t force a win or be forced to lose) W L L WWW W T WWW T D WWW D W

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (10) Garcia, Fall 2009 What did you mean “strongly solve”? Wargames (1983)

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (11) Garcia, Fall 2009 Weakly Solving A Game (Checkers) Endgame databases (solved) Master: main line of play to consider Workers: positions to search Log of Search Space Size Thanks to Jonathan Schaeffer for this slide…

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (12) Garcia, Fall 2009 Example: 1,2,…,10  Rules (on your turn):  Running total = 0  Rules (on your turn):  Add 1 or 2 to running total  Goal  Be the FIRST to get to 10  Example  Ana: “2 to make it 2”  Bob: “1 to make it 3”  Ana: “2 to make it 5”  Bob: “2 to make it 7”  photo  Ana: “1 to make it 8”  Bob: “2 to make it 10” I WIN! 7 ducks (out of 10)

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (13) Garcia, Fall 2009 Example: Tic-Tac-Toe  Rules (on your turn):  Place your X or O in an empty slot on 3x3 board  Goal  If your make 3-in-a-row first in any row / column / diag, win  Else if board is full with no 3-in-row, tie  Misére is tricky  3-in-row LOSES  Pair up and play now, then swap who goes 1st Values Visualization for Tic-Tac-Toe

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (14) Garcia, Fall 2009 Tic-Tac-Toe Answer Visualized!  Recursive Values Visualization Image  Misére Tic-tac-toe  Outer rim is position  Inner levels moves  Legend Lose Tie Win Misére Tic-Tac-Toe 2-ply Answer

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (15) Garcia, Fall 2009 Computational Game Theory  Large games  Can theorize strategies, build AI systems to play  Using “Endgame databases”  Can study endgames, smaller version of orig  Examples: Quick Chess, 9x9 Go, 6x6 Checkers, etc.  Can put 18 years into a game [Schaeffer, Checkers]  Small-to-medium games  Can have computer strongly solve and…  Play against it and teach us strategy  Allow us to test our theories on the database, analysis  Analyze human-human game and tell us where we erred!  Big goal: Hunt Big Game – those not solved yet  I wrote GAMESMAN in 1988 (almost 20 yrs ago!), the basis of my GamesCrafters research group

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (16) Garcia, Fall 2009 GamesCraftersGamesCrafters.berkeley.edu  Undergraduate Computational Game Theory Research Group  250+ students since 2001  We now average 20/semester!  They work in teams of 2+  Most return, take more senior roles (sub-group team leads)  Maximization (bottom-up solve)  Oh, DeepaBlue (parallelization)  GUI (graphical interface work)  Retro (GUI refactoring)  Architecture (core)  New/ice Games (add / refactor)  Documentation (games & code)

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (17) Garcia, Fall 2009 GamesCrafters  Projects span CS areas  AI : Writing “intelligent” players  DB: How do we store results?  HCI: Implementing interfaces  Graphics: Values visualizations  SE: Lots of SE juice here, it’s big!  Defining & implementing APIs  Managing open source SW  OS: We have our own VM  Also eHarmony & net DB  PL: We’re defining languages to describes games and GUIs  THY: Lots of combinatorics here: position & move hash functions  Perennial Cal Day favorite!  “Research and Development can be fun?!” Lines of Code: 8KJava 80KTcl/Tk 155KC

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (18) Garcia, Fall 2009 Alumni Feedback  Student feedback (2006 student report)  Problem: “Undergrads find it hard to participate in research”  Solution: “Create more activities like [Dan’s groups]”  “It pulled together all of the theoretical concepts from the various CS classes in providing my first practical application of my degree. Everything I learned in class was also present in GamesCrafters.”  “I learned more about real software engineering in GamesCrafters than in my CS classes combined”  “GamesCrafters was the defining institution of my undergraduate career at Cal.” 2009Sp GamesCrafters

UC Berkeley CS39N “The Beauty and Joy of Computing” : Computational Game Theory (19) Garcia, Fall 2009  Board games are exponential in nature  So has been the progress of the speed / capacity of computers!  Therefore, every few years, we only get to solve one more “ply”  One by one, we’re going to solve them and/or beat humans  We’ll never solve some  E.g., hardest game : Go Future Go’s search space ~