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《人工智能（英文版）》包含当前人工智能（AI）研究的主要内容，尤其强调实际应用，涉及数据挖掘等许多最新应用领域。全书共13章，分别讲述了AI的历史、不用知识的搜索、用知识的搜索、AI与博弈、知识表示、机器学习、演化计算、神经网络I、机器人学与AI、智能Agent、来自生物的模型与混合模型以及AⅡ语言。《人工智能（英文版）》给出了算法的较详细实现，与现有的以理论基础为核心的大多数经典人工智能著作相比，《人工智能（英文版）》有自身的鲜明特色，且内容与国内人工智能课程的教学内容吻合，尤其有利于培养学生解决人工智能实际问题的能力。

《人工智能（英文版）》适合高等学校计算机、自动化等信息学科的本科生和研究生阅读，也适合广大人工智能爱好者自学使用，《人工智能（英文版）》也能为人工智能研究人员了解各种算法的设计思路和具体实现框架提供参考。

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　ExercisesChapter 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　ExercisesChapter 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　ExercisesChapter 4 AI and Games　Two-Player Games　The Minimax Algorithm　Classical Game AI　Video Game AI　Chapter Summary　References　Resources　ExercisesChapter 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　ExercisesChapter 6 Machine Learning　Machine-Learning Algorithms　Chapter Summary　Resources　ExercisesChapter 7 Evolutionary Computation Short History of Evolutionary Computation　Biological Motivation Genetic Algorithms (GA) Genetic Programming (GP) Evolutionary Strategies (ES) Differential Evolution (DE) Particle Swarm Optimization (PSO) Evolvable Hardware Chapter Summary References Resources ExercisesChapter 8 Neural Networks I Short History of Neural Networks Biological Motivation Fundamentals of Neural Networks The Perceptron Least-Mean-Square (LMS) Learning Learning with Backpropagation Probabilistic Neural Networks (PNN) Other Neural Network Architectures Tips for Building Neural Networks Chapter Summary References ExercisesChapter 9 Neural Networks II Unsupervised Learning Hebbian Learning Simple Competitive Learning K-Means Clustering Adaptive Resonance Theory (ART) Hopfield Auto-Associative Model Chapter Summary References ExercisesChapter 10 Robotics and AI Introduction to Robotics Braitenburg Vehicles Natural Sensing and Control Perception with Sensors Actuation with Effectors Robotic Control Systems Simple Control Architectures Movement Planning Group or Distributed Robotics Robot Programming Languages Robot Simulators Chapter Summary References Resources ExercisesChapter 11 Intelligent Agents Anatomy of an Agent Agent Properties and AI Agent Environments Agent Taxonomies Agent Architectures Agent Languages Agent Communication ACL (FIPA Agent Communication Language) Chapter Summary Resources References ExercisesChapter 12 Biologically Inspired and Hybrid Models Cellular Automata (CA) Artificial Immune Systems Artificial Life Fuzzy Systems Evolutionary Neural Networks Ant Colony Optimization (ACO) Affective Computing ResourcesChapter 13 The Languages of AI Language Taxonomy Languages of AI Other Languages Chapter Summary References Resources Exercises

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《人工智能（英文版）》包含当前人工智能（AI）研究的主要内容，尤其强调实际应用，涉及数据挖掘等许多最新应用领域。全书共13章，分别讲述了AI的历史、不用知识的搜索、用知识的搜索、AI与博弈、知识表示、机器学习、演化计算、神经网络I、机器人学与AI、智能Agent、来自生物的模型与混合模型以及AⅡ语言。《人工智能（英文版）》给出了算法的较详细实现，与现有的以理论基础为核心的大多数经典人工智能著作相比，《人工智能（英文版）》有自身的鲜明特色，且内容与国内人工智能课程的教学内容吻合，尤其有利于培养学生解决人工智能实际问题的能力。

《人工智能（英文版）》适合高等学校计算机、自动化等信息学科的本科生和研究生阅读，也适合广大人工智能爱好者自学使用，《人工智能（英文版）》也能为人工智能研究人员了解各种算法的设计思路和具体实现框架提供参考。