'Minimax algorithm' presentation slideshows

Heuristic Search

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

894 views • 78 slides

Heuristic Search

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

1.01k views • 78 slides

Game-Playing & Adversarial Search

This lecture topic: Game-Playing & Adversarial Search (two lectures) Chapter 5.1-5.5 Next lecture topic: Constraint Satisfaction Problems (two lectures) Chapter 6.1-6.4, except 6.3.3 (Please read lecture topic material before and after each lecture on that topic).

815 views • 68 slides

15-381 / 681 Instructors: Fei Fang (This Lecture) and Dave Touretzky feifang@cmu

Artificial Intelligence: Representation and Problem Solving Multi-agent Systems (1): Adversarial Search. 15-381 / 681 Instructors: Fei Fang (This Lecture) and Dave Touretzky feifang@cmu.edu Wean Hall 4126. Recap. Search/Satisfiability/Optimization/Deterministic and Symbolic Reasoning

712 views • 50 slides

Artificial Intelligence Adversarial search

Artificial Intelligence Adversarial search. Fall 2008 professor: Luigi Ceccaroni. Planning ahead in a world that includes a hostile agent. Games as search problems Idealization and simplification: Two players Alternate moves MAX player MIN player Available information:

592 views • 36 slides

Artificial Intelligence Adversarial search

Artificial Intelligence Adversarial search. Fall 2008 professor: Luigi Ceccaroni. Planning ahead in a world that includes a hostile agent. Games as search problems Idealization and simplification: Two players Alternate moves MAX player MIN player Available information:

380 views • 36 slides

Applied Neuro-Dynamic Programming in the Game of Chess

Applied Neuro-Dynamic Programming in the Game of Chess. James Gideon. Dynamic Programming (DP). Family of algorithms applied to problems where decisions are made in stages and a reward or cost is received at each stage that is additive over time Optimal control method Example:

549 views • 33 slides

Adversarial Search

f1(s) = (number of white queens) (number of black queens), etc. Other features which ... Othello: human champions refuse to compete against computers, who are ...

687 views • 30 slides

An Introduction to Artificial Intelligence

336 views • 24 slides

Chapter 6 Adversarial Search

Tehran Technical College of Shariaty Fall 2008. Chapter 6 Adversarial Search. Ehsan Adeli (eadeli@iust.ac.ir). Outline. Optimal decisions ?-? pruning Imperfect, real-time decisions. Games vs. search problems.

381 views • 22 slides

Intelligent Agents for A Hockey Game

Intelligent Agents for A Hockey Game. Presented by : Wen. 1. Outline. Outline Introduction 1.1 Proposal 1.2 Statement of The Problem 1.3 Solution of The Problem Background Knowledge 2.1 Intelligent Agents (IA) 2.2 Decision Rules 2.3 Minimax Algorithm AI in Hockey Game

359 views • 22 slides

Adversarial Search

281 views • 21 slides

Adversarial Search

Adversarial Search. Chapter 5.1-5.3. Games vs. search problems. "Unpredictable" opponent ? specifying a move for every possible opponent reply Time limits ? unlikely to find goal, must approximate . Game search.

331 views • 19 slides

Adversarial Search

Adversarial Search. Chapter 5.1-5.3. Games vs. search problems. "Unpredictable" opponent ? specifying a move for every possible opponent reply Time limits ? unlikely to find goal, must approximate . Game search.

217 views • 19 slides

Minimax

412 views • 17 slides

Backtracking and Games

Backtracking and Games. Eric Roberts CS 106B January 28, 2013. Searching in a Branching Structure.

300 views • 16 slides

Game Trees

Kat Powell Daniel Christopher Igor Polyakov CS 134 Spring 2007. Game Trees. Each vertex (or node) represents a game state (point of choice for a player) Children of each node are positions that are reached by one move

820 views • 7 slides