Lecture-07-Adversarial search

Lecture-07-Adversarial search - CS 561: Artificial...

Info iconThis preview shows pages 1–11. Sign up to view the full content.

View Full Document Right Arrow Icon
CS 561: Artificial Intelligence Instructor: Sofus A. Macskassy, macskass@usc.edu TAs: Nadeesha Ranashinghe ( nadeeshr@usc.edu ) William Yeoh ( wyeoh@usc.edu ) Harris Chiu ( chiciu@usc.edu ) Lectures: MW 5:00-6:20pm, OHE 122 / DEN Office hours: By appointment Class page: http://www-rcf.usc.edu/~macskass/CS561-Spring2010/ This class will use http://www.uscden.net/ and class webpage - Up to date information - Lecture notes - Relevant dates, links, etc. Course material: [AIMA] Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig. (2nd ed)
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This time: Outline (Adversarial Search AIMA Ch. 6] Game playing Perfect play The minimax algorithm alpha-beta pruning Resource limitations Elements of chance Imperfect information 2 CS561 - Lecture 7 - Macskassy - Spring 2010
Background image of page 2
What kind of games? Abstraction : To describe a game we must capture every relevant aspect of the game. Such as: Chess Tic-tac-toe Accessible environments: Such games are characterized by perfect information Search: game-playing then consists of a search through possible game positions Unpredictable opponent: introduces uncertainty thus game-playing must deal with contingency problems 3 CS561 - Lecture 7 - Macskassy - Spring 2010
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Searching for the next move Complexity: many games have a huge search space Chess: b = 35, m=100 nodes = 35 100 if each node takes about 1 ns to explore then each move will take about 10 50 millennia to calculate. Resource (e.g., time, memory) limit: optimal solution not feasible/possible, thus must approximate 1. Pruning: makes the search more efficient by discarding portions of the search tree that cannot improve quality result. 2. Evaluation functions: heuristics to evaluate utility of a state without exhaustive search. 4 CS561 - Lecture 7 - Macskassy - Spring 2010
Background image of page 4
Two-player games A game formulated as a search problem: Initial state: ? Operators: ? Terminal state: ? Utility function: ? 5 CS561 - Lecture 7 - Macskassy - Spring 2010
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Two-player games A game formulated as a search problem: Initial state: board position and turn Operators: definition of legal moves Terminal state: conditions for when game is over Utility function: a numeric value that describes the outcome of the game. E.g., -1, 0, 1 for loss, draw, win. (AKA payoff function ) 6 CS561 - Lecture 7 - Macskassy - Spring 2010
Background image of page 6
CS561 - Lecture 7 - Macskassy - Spring 2010 7 Games vs. search problems “Unpredictable" opponent solution is a strategy specifying a move for every possible opponent reply Time limits unlikely to find goal, must approximate Plan of attack: Computer considers possible lines of play (Babbage, 1846) Algorithm for perfect play (Zermelo, 1912; Von Neumann, 1944) Finite horizon, approximate evaluation (Zuse, 1945; Wiener, 1948; Shannon, 1950) First chess program (Turing, 1951) Machine learning to improve evaluation accuracy (Samuel, 1952- 57) Pruning to allow deeper search (McCarthy, 1956)
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Example: Tic-Tac-Toe 8 CS561 - Lecture 7 - Macskassy - Spring 2010
Background image of page 8
Type of games 9 CS561 - Lecture 7 - Macskassy - Spring 2010
Background image of page 9

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Type of games
Background image of page 10
Image of page 11
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 08/26/2010 for the course CSCI 561 taught by Professor Staff during the Spring '08 term at USC.

Page1 / 45

Lecture-07-Adversarial search - CS 561: Artificial...

This preview shows document pages 1 - 11. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online