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AlphaGo simplified : = rule-based AI and deep learning in everyday games /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	AlphaGo simplified :/ Mark Liu.
Reminder of title:	rule-based AI and deep learning in everyday games /
Author:	Liu, Mark
Published:	Boca Raton :CRC Press, : c2025.,
Description:	xxix, 377 p. :col. ill. ; : 26 cm.;
Notes:	"A Chapman & Hall book."
Subject:	Games - Design and construction. -
ISBN:	9781032722214

AlphaGo simplified : = rule-based AI and deep learning in everyday games /
Liu, Mark

AlphaGo simplified :rule-based AI and deep learning in everyday games /Mark Liu. - 1st ed. - Boca Raton :CRC Press,c2025. - xxix, 377 p. :col. ill. ;26 cm.

"A Chapman & Hall book."

Includes bibliographical references (p. 367) and index.

"May 11, 1997, was a watershed moment in the history of artificial intelligence (AI): the IBM supercomputer chess engine, Deep Blue, beat the world Chess champion, Garry Kasparov. It was the first time a machine has triumphed over a human player in a Chess tournament. Fast forward 19 years to May 9, 2016, DeepMind's AlphaGo beat the world Go champion Lee Sedol. AI again stole the spotlight and generated a media frenzy. This time, a new type of AI algorithm, namely machine learning (ML) was the driving force behind the game strategies. What exactly is ML? How is it related to AI? Why is deep learning (DL) so popular these days? This book explains how traditional rule-based AI and ML work and how they can be implemented in everyday games such as Last Coin standing, Tic Tac Toe, or Connect Four. Game rules in these three games are easy to implement. As a result, readers will learn rule-based AI, deep reinforcement learning, and more importantly, how to combine the two to create powerful game strategies (the whole is indeed greater than the sum of its parts) without getting bogged down in complicated game rules. Implementing rule-based AI and ML in these three simple games is fast and not computationally costly. As a result, game strategies can be trained in a matter of minutes or hours without the need for GPU training or supercomputing facilities, witnessing AI achieve superhuman performance in these three games. More importantly, readers will master the ideas behind rule-based AI such as the MiniMax algorithm, alpha-beta pruning, and Monte Carlo Tree Search (MCTS) and combine them with state-of-the-art ML techniques such as convolutional neural networks and deep reinforcement learning and apply them their own business fields and solve real-world problems. Explaining principles from the ground up, this book is appealing to both the general readership and industry professionals who could benefit from learning rule-based AI and deep reinforcement learning, as well as students and course convenors of computer science and programming"--

ISBN: 9781032722214

LCCN: 2024008658Subjects--Topical Terms:

891681
Games
--Design and construction.

LC Class. No.: GV1230 / .L58 2024

Dewey Class. No.: 794.8/3

AlphaGo simplified : = rule-based AI and deep learning in everyday games /
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250 $a 1st ed.
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300 $a xxix, 377 p. : $b col. ill. ; $c 26 cm.
500 $a "A Chapman & Hall book."
504 $a Includes bibliographical references (p. 367) and index.
520 # $a "May 11, 1997, was a watershed moment in the history of artificial intelligence (AI): the IBM supercomputer chess engine, Deep Blue, beat the world Chess champion, Garry Kasparov. It was the first time a machine has triumphed over a human player in a Chess tournament. Fast forward 19 years to May 9, 2016, DeepMind's AlphaGo beat the world Go champion Lee Sedol. AI again stole the spotlight and generated a media frenzy. This time, a new type of AI algorithm, namely machine learning (ML) was the driving force behind the game strategies. What exactly is ML? How is it related to AI? Why is deep learning (DL) so popular these days? This book explains how traditional rule-based AI and ML work and how they can be implemented in everyday games such as Last Coin standing, Tic Tac Toe, or Connect Four. Game rules in these three games are easy to implement. As a result, readers will learn rule-based AI, deep reinforcement learning, and more importantly, how to combine the two to create powerful game strategies (the whole is indeed greater than the sum of its parts) without getting bogged down in complicated game rules. Implementing rule-based AI and ML in these three simple games is fast and not computationally costly. As a result, game strategies can be trained in a matter of minutes or hours without the need for GPU training or supercomputing facilities, witnessing AI achieve superhuman performance in these three games. More importantly, readers will master the ideas behind rule-based AI such as the MiniMax algorithm, alpha-beta pruning, and Monte Carlo Tree Search (MCTS) and combine them with state-of-the-art ML techniques such as convolutional neural networks and deep reinforcement learning and apply them their own business fields and solve real-world problems. Explaining principles from the ground up, this book is appealing to both the general readership and industry professionals who could benefit from learning rule-based AI and deep reinforcement learning, as well as students and course convenors of computer science and programming"-- $c Provided by publisher.
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