'Board position' presentation slideshows

Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

IST 511 Information Management: Information and Technology Machine Learning. Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology The Pennsylvania State University University Park, PA, USA giles@ist.psu.edu http://clgiles.ist.psu.edu.

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http:// www.youtube.com / watch?v = LcPWEMwGJVQ

http:// www.youtube.com / watch?v = LcPWEMwGJVQ. Adversarial Search. CS311 David Kauchak Spring 2013. Some material borrowed from : Sara Owsley Sood and others. Admin. Reading/book? Assignment 2 On the web page 3 parts Anyone looking for a partner? Get started! Written assignments

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Minimax search algorithm

Minimax search algorithm For now, we assume that exhaustive search is possible. Generate the entire game tree. Assume it has depth d . For each terminal state, apply the payoff function to get its score.

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CS 60050 Machine Learning

CS 60050 Machine Learning. What is Machine Learning?. Adapt to / learn from data To optimize a performance function Can be used to: Extract knowledge from data Learn tasks that are difficult to formalise Create software that improves over time. When to learn

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

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Machine Learning Introduction

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IT/CS 811 Principles of Machine Learning and Inference

IT/CS 811 Principles of Machine Learning and Inference. 3. Rote Learning. Prof. Gheorghe Tecuci. Learning Agents Laboratory Computer Science Department George Mason University. Overview. Rote learning issues. Game playing as a performance task. Rote learning in game paying.

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Reinforcement Learning

Reinforcement Learning. Guest Lecturer: Chengxiang Zhai 15-681 Machine Learning December 6, 2001. Outline For Today. The Reinforcement Learning Problem Markov Decision Process Q-Learning Summary. The Checker Problem Revisited. Goal: To win every game!

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ICT Coordinators Information Day

ICT Coordinators Information Day. Term 3 2008. Agenda. Wrap Up of Term 2 - 2008 ICT Day Term 3 2008 Technology Update Sydney Region initiatives, NGEN, E-mail for Students Digital Education Revolution, STEMS, logging Service Desk calls via internet/portal

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Genetic Algorithms (Evolutionary Computing)

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Levee Board Consolidation Legislation Southeast Louisiana and Greater New Orleans

Levee Board Consolidation Legislation Southeast Louisiana and Greater New Orleans. Objectives. To demand that focus be on flood related duties To remove opportunities for patronage To create regional levee protection To provide constitutional protection of the new district.

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Game-playing AIs Part 1

Game-playing AIs Part 1. CIS 391 Fall 2007. Games: Outline of Unit. Part I (this set of slides) Motivation Game Trees Evaluation Functions Part II The Minimax Rule Alpha-Beta Pruning Game-playing AI successes. Motivation. Games are a form of multi-agent environment

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ADVERSARIAL GAME SEARCH: Min-Max Search

ADVERSARIAL GAME SEARCH: Min-Max Search. X. O. O. X. X. O. Between 2 adversaries Input: A board position Output: Best move (actually, the score for moving there) Objective function: board position evaluated to a number (may be +-1, 0) higher the number better the move: maximization

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AACRAO & RMACRAO 101 What is this, and how do I get involved?

AACRAO & RMACRAO 101 What is this, and how do I get involved?. Dr. Stan DeMerritt , Associate Academic Vice President Wayland Baptist University Kelley Christman, Associate Registrar University of Northern Colorado Lara Medley, Registrar Colorado School of Mines

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