| 本书为广大学生和人工智能开发人员提供了学习人工智能相关概念的一种新思路。 书中包含人工智能在多个领域的许多最新应用，这些领域包括游戏程序设计、群体智能、智能Agent、神经网络、人工免疫系统、遗传算法、模式识别、数值优化以及数据挖掘等。本书还讨论了从早期的LISP语言到近期的Python语言等多种人工智能语言。书中不仅包括了人工智能的理论和主要课题，还介绍了从数据输入到转换再到数据输出（即算法的使用）的实际需要的信息。因为传统的人工智能概念目前仅仅表示算法的各种类型，因此需要用一种不同的方法来介绍人工智能算法。这种“传感器－算法－效应器”的方法为这些算法提供了一个基础环境，能够帮助学生和人工智能从业者更好地理解它们，从而更好地应用这些算法。 |
| Chapter 1 The History of AI What is Intelligence? The Search for Mechanical Intelligence The Very Early Days (the early 1950s) Artificial Intelligence Emerges as a Field AI's Winter AI Re-emerges AI Inter-Disciplinary R&D Systems Approach Overview of this Book Chapter Summary References Resources Exercises Chapter 2 Uninformed Search Search and AI Classes of Search General State Space Search Trees, Graphs, and Representation Uninformed Search Improvements Algorithm Advantages Chapter Summary Algorithms Summary References Exercises Chapter 3 Informed Search Informed Search Best-First Search (Best-FS) A* Search Hill-Climbing Search Simulated Annealing (SA) Tabu Search Constraint Satisfaction Problems (CSP) Constraint Satisfaction Algorithms Chapter Summary Algorithms Summary References Resources Exercises Chapter 4 AI and Games Two-Player Games The Minimax Algorithm Classical Game AI Video Game AI Chapter Summary References Resources Exercises Chapter 5 Knowledge Representation Introduction Types of Knowledge The Role of Knowledge Semantic Networks Frames Propositional Logic First-Order Logic (Predicate Logic) Semantic Web Computational Knowledge Discovery Ontology Communication of Knowledge Chapter Summary References Resources Exercises Chapter 6 Machine Learning Machine-Learning Algorithms Chapter Summary Resources Exercises Chapter 7 Evolutionary Computation Short History of Evolutionary Computation Biological Motivation …… Chapter 8 Neural Networks I Chapter 9 Neural Networks II Chapter 10 Robotics and AI Chapter 11 Intelligent Agents Chapter 12 Biologically Inspired and Hybrid Models Chapter 13 The Languages of AI |
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