人工智能

作者简介

《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。

书籍目录

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

编辑推荐

《人工智能（英文版）》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。《人工智能（英文版）》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。

内容概要

作者：(美国)琼斯

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