Instability of Scoring Heuristic In games with value exchange, the heuristics are very bumpy Make smoothing assumptions search for "quiesence"

Similar documents
Adversary Search. Ref: Chapter 5

16.410/413 Principles of Autonomy and Decision Making

game tree complete all possible moves

Adversarial Search and Game- Playing C H A P T E R 6 C M P T : S P R I N G H A S S A N K H O S R A V I

Playing Games. Henry Z. Lo. June 23, We consider writing AI to play games with the following properties:

Module 3. Problem Solving using Search- (Two agent) Version 2 CSE IIT, Kharagpur

CS510 \ Lecture Ariel Stolerman

Set 4: Game-Playing. ICS 271 Fall 2017 Kalev Kask

Pengju

Adversarial Search Aka Games

ARTIFICIAL INTELLIGENCE (CS 370D)

CS 4700: Artificial Intelligence

Adversarial Search 1

Game-Playing & Adversarial Search

Artificial Intelligence. Minimax and alpha-beta pruning

Lecture 14. Questions? Friday, February 10 CS 430 Artificial Intelligence - Lecture 14 1

Game Tree Search. CSC384: Introduction to Artificial Intelligence. Generalizing Search Problem. General Games. What makes something a game?

2/5/17 ADVERSARIAL SEARCH. Today. Introduce adversarial games Minimax as an optimal strategy Alpha-beta pruning Real-time decision making

CS 2710 Foundations of AI. Lecture 9. Adversarial search. CS 2710 Foundations of AI. Game search

CS 440 / ECE 448 Introduction to Artificial Intelligence Spring 2010 Lecture #5

Microeconomics of Banking: Lecture 4

Multiple Agents. Why can t we all just get along? (Rodney King)

Algorithms for Data Structures: Search for Games. Phillip Smith 27/11/13

Adversarial Search. CS 486/686: Introduction to Artificial Intelligence

Adversarial Search. Human-aware Robotics. 2018/01/25 Chapter 5 in R&N 3rd Ø Announcement: Slides for this lecture are here:

Chapter 3 Learning in Two-Player Matrix Games

Learning to Play like an Othello Master CS 229 Project Report. Shir Aharon, Amanda Chang, Kent Koyanagi

Game Playing State-of-the-Art

Adversarial Search and Game Playing. Russell and Norvig: Chapter 5

Game Theory Lecturer: Ji Liu Thanks for Jerry Zhu's slides

4. Games and search. Lecture Artificial Intelligence (4ov / 8op)

More on games (Ch )

CS 1571 Introduction to AI Lecture 12. Adversarial search. CS 1571 Intro to AI. Announcements

CS885 Reinforcement Learning Lecture 13c: June 13, Adversarial Search [RusNor] Sec

Last update: March 9, Game playing. CMSC 421, Chapter 6. CMSC 421, Chapter 6 1

More on games (Ch )

Introduction to (Networked) Game Theory. Networked Life NETS 112 Fall 2016 Prof. Michael Kearns

ADVERSARIAL SEARCH. Today. Reading. Goals. AIMA Chapter , 5.7,5.8

Adversarial Search and Game Theory. CS 510 Lecture 5 October 26, 2017

Foundations of AI. 5. Board Games. Search Strategies for Games, Games with Chance, State of the Art. Wolfram Burgard and Luc De Raedt SA-1

CS 5522: Artificial Intelligence II

Game Playing AI. Dr. Baldassano Yu s Elite Education

Adversarial Search. CS 486/686: Introduction to Artificial Intelligence

Game-playing AIs: Games and Adversarial Search FINAL SET (w/ pruning study examples) AIMA

Games CSE 473. Kasparov Vs. Deep Junior August 2, 2003 Match ends in a 3 / 3 tie!

Distributed Optimization and Games

Computer Science and Software Engineering University of Wisconsin - Platteville. 4. Game Play. CS 3030 Lecture Notes Yan Shi UW-Platteville

Adversarial search (game playing)

Foundations of AI. 6. Adversarial Search. Search Strategies for Games, Games with Chance, State of the Art. Wolfram Burgard & Bernhard Nebel

CPS331 Lecture: Search in Games last revised 2/16/10

Contents. MA 327/ECO 327 Introduction to Game Theory Fall 2017 Notes. 1 Wednesday, August Friday, August Monday, August 28 6

Introduction to Game Theory

2 person perfect information

Adversarial Search. Rob Platt Northeastern University. Some images and slides are used from: AIMA CS188 UC Berkeley

Computer Game Programming Board Games

Adversarial Search Lecture 7

Game-playing AIs: Games and Adversarial Search I AIMA

CS188 Spring 2010 Section 3: Game Trees

Chapter 15: Game Theory: The Mathematics of Competition Lesson Plan

Adversarial Search. Read AIMA Chapter CIS 421/521 - Intro to AI 1

Multi-player, non-zero-sum games

ECO 463. SimultaneousGames

UMBC CMSC 671 Midterm Exam 22 October 2012

CS188 Spring 2014 Section 3: Games

Math 611: Game Theory Notes Chetan Prakash 2012

CSC 380 Final Presentation. Connect 4 David Alligood, Scott Swiger, Jo Van Voorhis

CS 380: ARTIFICIAL INTELLIGENCE ADVERSARIAL SEARCH. Santiago Ontañón

Mixed Strategies; Maxmin

Announcements. Homework 1. Project 1. Due tonight at 11:59pm. Due Friday 2/8 at 4:00pm. Electronic HW1 Written HW1

Game Playing State-of-the-Art. CS 188: Artificial Intelligence. Behavior from Computation. Video of Demo Mystery Pacman. Adversarial Search

Minmax and Dominance

mywbut.com Two agent games : alpha beta pruning

Theory and Practice of Artificial Intelligence

ADVERSARIAL SEARCH. Today. Reading. Goals. AIMA Chapter Read , Skim 5.7

Introduction to (Networked) Game Theory. Networked Life NETS 112 Fall 2014 Prof. Michael Kearns

Adversarial Search (Game Playing)

CSC384: Introduction to Artificial Intelligence. Game Tree Search

The Player Of Games Culture 2 Iain M Banks

Programming Project 1: Pacman (Due )

Programming an Othello AI Michael An (man4), Evan Liang (liange)

Game Tree Search. Generalizing Search Problems. Two-person Zero-Sum Games. Generalizing Search Problems. CSC384: Intro to Artificial Intelligence

Using Artificial intelligent to solve the game of 2048

Foundations of Artificial Intelligence

CS 188: Artificial Intelligence Spring 2007

Distributed Optimization and Games

CS 188: Artificial Intelligence

DECISION MAKING GAME THEORY

Section Notes 6. Game Theory. Applied Math 121. Week of March 22, understand the difference between pure and mixed strategies.

Artificial Intelligence

ADVERSARIAL SEARCH. Chapter 5

CMPUT 396 Tic-Tac-Toe Game

Game Theory and Randomized Algorithms

ECON 282 Final Practice Problems

Game Playing. Dr. Richard J. Povinelli. Page 1. rev 1.1, 9/14/2003

Artificial Intelligence Lecture 3

1\2 L m R M 2, 2 1, 1 0, 0 B 1, 0 0, 0 1, 1

School of EECS Washington State University. Artificial Intelligence

CS 380: ARTIFICIAL INTELLIGENCE

Artificial Intelligence Adversarial Search

AI Approaches to Ultimate Tic-Tac-Toe

Transcription:

More on games Gaming Complications Instability of Scoring Heuristic In games with value exchange, the heuristics are very bumpy Make smoothing assumptions search for "quiesence" The Horizon Effect No matter how deep you search, there may be a loss just beyond the horizon. Use secondary searching on a few final candidates

Knowledge Search Tradeoff One of the foundational principles of AI The more you know, the less you have to search. In minimax game playing with a static evaluation function, the "knowledge" estimates how good a position is. If it was "perfect knowledge" it would be equivalent to full unrolling of the game tree possible only for small games Search Random Testing Brute-Force examination Hill Climbing Organized Search Heuristic Search Locally informed methods "Strong" methods Mathematical Solutions Insight and Intuition more knowledge less search

Branch and Bound Algorithmic Technique When exploring multiple paths, use knowledge (of value, optimality, cost) of KNOWN paths to "prune" other branches: If you can prove that a branch of a search tree cannot POSSIBLY be better than another, dont search it! Alpha-Beta Pruning A variety of Branch and Bound for searching game trees If we are guaranteed a score by making move A, then don't bother searching responses to move B, once any response to B is less than our guarantee

What are Alpha and Beta Greatest Lower Bound on my score (worst you can do to me) Least Upper Bound on your score (Best I can do against you) These are used recursively in a flip-flop fashion A B C D 4 7 9 E F G H I J K L -2 0-3 -5 7 5-3 -7

= -3 Worst you can do if I choose move B A SAVES 2 NODES!!!!! k@-3 means D is no better B 4-3 C 7 D 9 E F G H I J K L -2 0-3 -5 7 5-3 -7 H@-5 means C is worse than B Iterative deepening First search to depth 1 Then search to depth 2 and so on Combined with some caching, iterative deepening is used in many game playing systems. Especially when playing with a clock!

Caching Each time you do a search, you are re-calculating a lot of boards and heuristics Find a way to keep some of those around to avoid re-calculation Related to Memoization Canonical Forms many games have symmetries player a or player b Rotation of board Mirror image of board Storing board in a canonical form will lessen memory and computational requirements Can the board be converted to an integer? comparison becomes faster "="

Games are an active research topic Initial attempts at Robotic Sports Robotic Olympics, Demolition Derby Video Games and Internet Games Real Time Performance No Robotics Problem Arbitrary sophistication Interactive Knowledge Games Are computers good at trivia? Machine Learning of Games More stuff Game Theory Horizon Effect Search vs Knowledge proof Alpha beta pruning Iterative Deepening Caching states Canonical forms Book Moves

Game Theory? AI has not focused on "Game Theory" Mathematical Model of Decisionmaking under adversary with cooperation Von Neumann, 1934 Nash 1950 (Nash Equilibria) Field of Mathematical Economics. What is a two-player game in normal form? Each player chooses a move and receives a payoff based on others zero Sum Win Lose Win 0 0 1-1 Lose -1 1 0 0 In a zero sum game, the sum of payoffs to all players, is zero.

Conversion from Extensive (Tree) to Normal (Table) Can be done for small games combinatorial explosion tho Positive Sum Games Possible (Iterated) Prisoner's (Choice is a) Dilemma prisoner dilemma cooperate defect cooperate 3 3 0 5 defect 5 0 1 1

Game Theory Concepts pure strategy vs mixed strategy A pure strategy is to always play one line or column A mixed strategy is to play according to a probability distribution Game Theory Concepts Nash Equilbrium If there is a set of strategies with the property that no player can benefit by changing her strategy while the other players keep their strategies unchanged, then that set of strategies and the corresponding payoffs constitute the Nash Equilibrium.

Value of Game Theory Extensive Literature Game of Chicken International relations MAD Nuclear Strategy evolution of cooperation positive sum games Prisoner's dilemma's Evolution (Maynard Smith) Hawks vs Doves Markets, bidding Organization and Team theory