Alphago Simplified: Rule-Based AI and Deep Learning in Everyday Games

Liu, Mark

  • 出版商: CRC
  • 出版日期: 2024-08-27
  • 售價: $2,290
  • 貴賓價: 9.5$2,176
  • 語言: 英文
  • 頁數: 378
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1032722126
  • ISBN-13: 9781032722122
  • 相關分類: 人工智慧DeepLearning
  • 海外代購書籍(需單獨結帳)

相關主題

商品描述

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 had 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 straightforward games is quick and not computationally intensive. Consequently, game strategies can be trained in mere minutes or hours without requiring GPU training or supercomputing facilities, showcasing AI's ability to achieve superhuman performance in these games. More importantly, readers will gain a thorough understanding of the principles behind rule-based AI, such as the MiniMax algorithm, alpha-beta pruning, and Monte Carlo Tree Search (MCTS), and how to integrate them with cutting-edge ML techniques like convolutional neural networks and deep reinforcement learning to apply them in their own business fields and tackle real-world challenges.

Written with clarity from the ground up, this book appeals to both general readers and industry professionals who seek to learn about rule-based AI and deep reinforcement learning, as well as students and educators in computer science and programming courses.

商品描述(中文翻譯)

1997年5月11日是人工智慧(AI)歷史上的一個重要時刻:IBM的超級電腦棋手Deep Blue擊敗了世界棋王加里·卡斯帕羅夫(Garry Kasparov)。這是機器首次在棋賽中戰勝人類選手。快轉到2016年5月9日,DeepMind的AlphaGo擊敗了世界圍棋冠軍李世石(Lee Sedol)。AI再次成為焦點,並引發了媒體的熱潮。這一次,一種新的AI算法,即機器學習(ML),成為了遊戲策略的推動力。

那麼,機器學習究竟是什麼?它與人工智慧有什麼關係?為什麼深度學習(DL)在當今如此受歡迎?本書解釋了傳統基於規則的AI和機器學習的運作方式,以及如何在日常遊戲中實施這些技術,例如《最後的硬幣》、《井字遊戲》或《四子連線》。這三款遊戲的規則易於實施。因此,讀者將學習基於規則的AI、深度強化學習,更重要的是,如何將兩者結合以創造強大的遊戲策略(整體確實大於部分之和),而不會陷入複雜的遊戲規則中。

在這些簡單的遊戲中實施基於規則的AI和機器學習是快速且不需要大量計算資源的。因此,遊戲策略可以在幾分鐘或幾小時內訓練完成,而無需GPU訓練或超級計算設施,展示了AI在這些遊戲中達到超人表現的能力。更重要的是,讀者將深入了解基於規則的AI背後的原則,例如MiniMax算法、α-β剪枝和蒙地卡羅樹搜索(MCTS),以及如何將它們與前沿的機器學習技術(如卷積神經網絡和深度強化學習)整合,以應用於自己的商業領域並解決現實世界的挑戰。

本書以清晰的方式從基礎開始撰寫,適合一般讀者和尋求了解基於規則的AI及深度強化學習的行業專業人士,以及計算機科學和程式設計課程的學生和教育工作者。

作者簡介

Mark H. Liu is an Associate Professor of Finance, the (Founding) Director of the MS Finance Program at the University of Kentucky. He obtained his Ph.D. in finance from Boston College in 2004 and his M.A. in economics from Western University in Canada in 1998. Dr. Liu has more than 20 years of coding experience and is the author of two books: Make Python Talk (No Starch Press, 2021) and Machine Learning, Animated (CRC Press, 2023).

作者簡介(中文翻譯)

馬克·H·劉是肯塔基大學金融學副教授及金融碩士課程的(創始)主任。他於2004年在波士頓大學獲得金融學博士學位,並於1998年在加拿大西安大略大學獲得經濟學碩士學位。劉博士擁有超過20年的程式編寫經驗,並且是兩本書的作者:《讓 Python 說話》(No Starch Press, 2021)和《機器學習,動畫版》(CRC Press, 2023)。