1 Heuristic Search 4 4.0Introduction 4.1An Algorithm for Heuristic Search 4.2Admissibility, Monotonicity, and Informedness 4.3Using Heuristics in Games.

Slides:



Advertisements
Similar presentations
Big Ideas in Cmput366. Search Blind Search State space representation Iterative deepening Heuristic Search A*, f(n)=g(n)+h(n), admissible heuristics Local.
Advertisements

Ch 4. Heuristic Search 4.0 Introduction(Heuristic)
Adversarial Search We have experience in search where we assume that we are the only intelligent being and we have explicit control over the “world”. Lets.
Informed Search Methods Copyright, 1996 © Dale Carnegie & Associates, Inc. Chapter 4 Spring 2004.
CSCE 580 ANDREW SMITH JOHNNY FLOWERS IDA* and Memory-Bounded Search Algorithms.
Using Heuristics in Games At that time two opposing concepts of the game called forth commentary and discussion. The foremost players distinguished two.
ICS-271:Notes 6: 1 Notes 6: Game-Playing ICS 271 Fall 2008.
Artificial Intelligence
CS 484 – Artificial Intelligence
Tic Tac Toe Architecture CSE 5290 – Artificial Intelligence 06/13/2011 Christopher Hepler.
Structures and Strategies For Space State Search
MINIMAX SEARCH AND ALPHA- BETA PRUNING: PLAYER 1 VS. PLAYER 2.
10/19/2004TCSS435A Isabelle Bichindaritz1 Game and Tree Searching.
State Space 4 Chapter 4 Adversarial Games. Two Flavors Games of Perfect Information ◦Each player knows everything that can be known ◦Chess, Othello Games.
HEURISTIC SEARCH ARTIFICIAL INTELLIGENCE 5th edition George F Luger
Mahgul Gulzai Moomal Umer Rabail Hafeez
This time: Outline Game playing The minimax algorithm
1 Game Playing Chapter 6 Additional references for the slides: Luger’s AI book (2005). Robert Wilensky’s CS188 slides:
1 Heuristic Search 4 4.0Introduction 4.1An Algorithm for Heuristic Search 4.2Admissibility, Monotonicity, and Informedness 4.3Using Heuristics in Games.
Game Playing CSC361 AI CSC361: Game Playing.
Min-Max Trees Based on slides by: Rob Powers Ian Gent Yishay Mansour.
Control and Implementation of State Space Search
ICS-271:Notes 6: 1 Notes 6: Game-Playing ICS 271 Fall 2006.
1 Heuristic Search 4 4.0Introduction 4.1An Algorithm for Heuristic Search 4.2Admissibility, Monotonicity, and Informedness 4.3Using Heuristics in Games.
Game Tree Search based on Russ Greiner and Jean-Claude Latombe’s notes.
Structures and Strategies For Space State Search
HEURISTIC SEARCH. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Portion of the state space for tic-tac-toe.
1 Heuristic Search 4 4.0Introduction 4.1An Algorithm for Heuristic Search 4.2Admissibility, Monotonicity, and Informedness 4.3Using Heuristics in Games.
Game Playing: Adversarial Search Chapter 6. Why study games Fun Clear criteria for success Interesting, hard problems which require minimal “initial structure”
CISC 235: Topic 6 Game Trees.
Game Playing. Introduction Why is game playing so interesting from an AI point of view? –Game Playing is harder then common searching The search space.
KU NLP Heuristic Search Heuristic Search and Expert Systems (1) q An interesting approach to implementing heuristics is the use of confidence.
Game Playing.
Search CSE When you can’t use A* Hill-climbing Simulated Annealing Other strategies 2 person- games.
1 Computer Group Engineering Department University of Science and Culture S. H. Davarpanah
Heuristic Search In addition to depth-first search, breadth-first search, bound depth-first search, and iterative deepening, we can also use informed or.
Chapter 6 Adversarial Search. Adversarial Search Problem Initial State Initial State Successor Function Successor Function Terminal Test Terminal Test.
Building Control Algorithms For State Space Search.
Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Lecture 9 of 14 Friday, 10 September.
George F Luger ARTIFICIAL INTELLIGENCE 5th edition Structures and Strategies for Complex Problem Solving HEURISTIC SEARCH Luger: Artificial Intelligence,
George F Luger ARTIFICIAL INTELLIGENCE 6th edition Structures and Strategies for Complex Problem Solving HEURISTIC SEARCH Luger: Artificial Intelligence,
Search exploring the consequences of possible actions.
HEURISTIC SEARCH.
CS 415 – A.I. Slide Set 6. Chapter 4 – Heuristic Search Heuristic – the study of the methods and rules of discovery and invention State Space Heuristics.
Game Playing. Introduction One of the earliest areas in artificial intelligence is game playing. Two-person zero-sum game. Games for which the state space.
Adversarial Search Chapter Games vs. search problems "Unpredictable" opponent  specifying a move for every possible opponent reply Time limits.
1 CSE 4705 Artificial Intelligence Jinbo Bi Department of Computer Science & Engineering
Knowledge Search CPTR 314.
George F Luger ARTIFICIAL INTELLIGENCE 5th edition Structures and Strategies for Complex Problem Solving Structures and Strategies For Space State Search.
George F Luger ARTIFICIAL INTELLIGENCE 5th edition Structures and Strategies for Complex Problem Solving HEURISTIC SEARCH Luger: Artificial Intelligence,
HEURISTIC SEARCH 4 4.0Introduction 4.1An Algorithm for Heuristic Search 4.2Admissibility, Monotonicity, and Informedness 4.3Using Heuristics in Games 4.4Complexity.
ARTIFICIAL INTELLIGENCE (CS 461D) Princess Nora University Faculty of Computer & Information Systems.
Adversarial Search. Game playing u Multi-agent competitive environment u The most common games are deterministic, turn- taking, two-player, zero-sum game.
STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH 3 3.0Introduction 3.1Graph Theory 3.2Strategies for State Space Search 3.3Using the State Space to Represent.
STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH 3 3.0Introduction 3.1Graph Theory 3.2Strategies for State Space Search 3.3Using the State Space to Represent.
CMSC 421: Intro to Artificial Intelligence October 6, 2003 Lecture 7: Games Professor: Bonnie J. Dorr TA: Nate Waisbrot.
Adversarial Search. Regular Tic Tac Toe Play a few games. –What is the expected outcome –What kinds of moves “guarantee” that?
D Goforth - COSC 4117, fall OK administrivia  Exam format – take home, open book  Suicide rule for King’s court Illegal moves cannot move last.
Adversarial Search In this lecture, we introduce a new search scenario: game playing 1.two players, 2.zero-sum game, (win-lose, lose-win, draw) 3.perfect.
Luca Weibel Honors Track: Competitive Programming & Problem Solving Partisan game theory.
Adversarial Search CMPT 463. When: Tuesday, April 5 3:30PM Where: RLC 105 Team based: one, two or three people per team Languages: Python, C++ and Java.
1 Chapter 6 Game Playing. 2 Chapter 6 Contents l Game Trees l Assumptions l Static evaluation functions l Searching game trees l Minimax l Bounded lookahead.
AI Classnotes #5, John Shieh, 2012
CS 460 Spring 2011 Lecture 4.
Heuristic Search A heuristic is a rule for choosing a branch in a state space search that will most likely lead to a problem solution Heuristics are used.
State Space 4 Chapter 4 Adversarial Games.
Game playing.
NIM - a two person game n objects are in one pile
4 Heuristic Search 4.0 Introduction
Game Playing Fifth Lecture 2019/4/11.
Presentation transcript:

1 Heuristic Search 4 4.0Introduction 4.1An Algorithm for Heuristic Search 4.2Admissibility, Monotonicity, and Informedness 4.3Using Heuristics in Games 4.4Complexity Issues 4.5Epilogue and References 4.6Exercises

2 State space for a variant of nim

3 Exhaustive minimax for the game of nim

4 Minimax applied to a hypothetical state space (Fig. 4.15)

5 Heuristic measuring conflict

6 Two-ply minimax applied to the opening move of tic-tac-toe (Nilsson, 1971)

7 Two-ply minimax and one of two possible second MAX moves (Nilsson, 1971)

8 Two-ply minimax applied to X’s move near the end of the game (Nilsson, 1971)

9 Alpha-beta pruning applied to the state space of Fig

10 Number of nodes generated as a function of branching factor B, and solution length L (Nilsson, 1980)

11 Informal plot of cost of searching and cost of computing heuristic evaluation against heuristic informedness (Nilsson, 1980)

12 Exercise 2. The sliding block puzzle

13 Exercise 13

14 Exercise 14