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人工智能

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Books Computers & Internet Artificial Intelligence

Jingdong book

人工智能

Name: 人工智能
Brand: Jingdong book
SKU: 3118018131
Price: 42.28 USD
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Content Description

《人工智能》教全面地介绍了人工智能领域的各种课题，其中包括使用神经网络和遗传算法的感知和适应性、附有本体论的智能agent、自动推理、自然语言分析，以及机器学习的随机途径。论述的许多技术和策略可用于应对现今计算机科学面临的众多挑战。此外，《人工智能》进一步从哲学、心理学和神经生理学等角度讨论了人工智能研究工作。
《人工智能》可作为高等院校研究生的教材，还可供从事人工智能研究和应用的专业人员参考。

Author Description

刘凤岐，国防科技大学原计算机系教授，长期从事计算机应用和人工智能的教学和研究工作。自上世纪80年代中期至上世纪末一直带领学生承担国家“863”计划中关于“人工智能程序设计与环境”、“专家系统开发工具”等的研究课题，获省、部级科技进步奖六项。发表论文20余篇，并编著了《逻辑程序设计原理和方法》。1990年获国家“中青年有突出贡献专家”称号，享受政府特殊津贴。

Catalogue

出版者的话
序言
前言
第1章导论
1.1 人工智能的定义与基础
1.1.1 何谓人工智能？
1.1.2 ai基础简史
1.1.3 ai与唯理主义和经验主义传统
1.1.4 形式逻辑的发展
1.1.5 图灵测试
1.1.6 智能的生物与社会模型agent理论
1.2 ai应用领域概述
1.2.1 博弈
1.2.2 自动推理与定理证明
1.2.3 专家系统
1.2.4 自然语言理解
1.2.5 模拟人的性能
1.2.6 规划与机器人
1.2.7 ai语言和环境
1.2.8 机器学习
1.2.9 神经网络与遗传算法
1.2.1 0ai与哲学
1.3 人工智能概要
练习

第2章表示与搜索导引
2.1 表示系统
2.2 搜索

第3章谓词演算
3.1 命题演算
3.1.1 语法
3.1.2 语义
3.2 谓词演算
3.2.1 语法
3.2.2 语义
3.3 谓词演算的推断规则
3.3.1 推断规则
3.3.2 一致化
3.4 应用：基于逻辑的家庭财务咨询系统
练习

第4章状态空间搜索
4.1 状态空间搜索的结构
4.1.1 图论
4.1.2 有限状态机
4.1.3 问题的状态空间表示
4.2 状态空间搜索策略
4.2.1 数据驱动和目标驱动的搜索
4.2.2 广度优先和深度优先搜索
4.2.3 逐步加深的深度优先搜索
4.3 与/或图和逻辑推理
4.4 应用：家庭财务咨询系统
练习

第5章启发式搜索
5.1 爬山法和动态规划
5.1.1 爬山法
5.1.2 动态规划
5.2 最好优先搜索算法
5.2.1 实现最好优先搜索
5.2.2 实现启发估价函数
5.2.3 启发式搜索与专家系统
5.3 可采纳性、单调性及信息度
5.3.1 可采纳性
5.3.2 单调性
5.3.3 a*算法的比较
5.4 搜索博弈图
5.4.1 极小极大程序
5.4.2 固定深度的minimax
5.4.3 αβ剪枝
5.5 计算复杂度问题
练习

第6章状态空间搜索的控制算法
6.1 基于递归的搜索
6.1.1 递归搜索
6.1.2 递归搜索示例：模式驱动的推理
6.2 产生式系统
6.2.1 定义及简史
6.2.2 产生式系统示例
6.2.3 搜索的控制
6.2.4 产生式系统的优点
6.3 问题求解的黑板体系
练习

第7章知识表示
7.1 ai表示研究简史
7.1.1 含义的联想主义理论
7.1.2 语义网络的早期工作
7.1.3 网络关系的标准化
7.1.4 脚本
7.1.5 框架
7.2 概念图
7.2.1 概念图导引
7.2.2 类型、个体和名称
7.2.3 类型的层次结构
7.2.4 概念图的操作
7.2.5 命题节点
7.2.6 概念图与逻辑
7.3 替代显式表示
7.3.1 brooks的包容体系
7.3.2 多种表示、本体论与知识服务
7.4 基于agent的分布式问题求解
7.4.1 面向agent的问题求解：一种定义
7.4.2 agent范型的示例及其存在问题
练习

第8章知识系统
8.1 专家系统技术概述
8.1.1 设计基于规则的专家系统
8.1.2 问题领域的选择与知识工程过程
8.1.3 概念模型及其在知识获取中的作用
8.2 基于规则的专家系统
8.2.1 产生式系统与目标驱动的问题求解
8.2.2 目标驱动的推理的解释与透明性
8.2.3 产生式系统与数据驱动的推理
8.2.4 专家系统的启发性与控制
8.3 基于模型、基于事例和混合系统
8.3.1 基于模型的推理导引
8.3.2 基于模型的推理：nasa的例子
8.3.3 基于事例的推理导引
8.3.4 混合设计
8.4 规划
8.4.1 strips
8.4.2 目的反应式规划
8.4.3 规划：nasa的例子
练习

第9章不确定推理
9.1 基于逻辑的反绎推理
9.1.1 非单调推理逻辑
9.1.2 真值维护系统
9.1.3 基于最小模型的逻辑
9.1.4 集合覆盖与基于逻辑的反绎
9.2 反绎：替代逻辑
9.2.1 肯定因数代数
9.2.2 模糊推理
9.2.3 dempster和shafer的证据论
9.3 不确定性的随机途径
9.3.1 有向图模型：贝叶斯信念网络
9.3.2 有向图模型：d分隔
9.3.3 有向图模型：推理算法
9.3.4 有向图模型：动态贝叶斯网络
9.3.5 markov模型：离散markov过程
9.3.6 markov模型：改型
练习

第10章基于符号的机器学习
10.1 基于符号的学习框架
10.2 版本空间搜索
10.2.1 一般化操作与概念空间
10.2.2 候选排除算法
10.2.3 lex：归纳搜索启发式
10.2.4 候选排除算法的评价
10.3 id3决策树归纳算法
10.3.1 自顶向下归纳决策树
10.3.2 信息论的测试选择
10.3.3 评价id3
10.3.4 决策树的数据问题
10.4 归纳偏向与学习能力
10.4.1 归纳偏向
10.4.2 学习能力理论
10.5 知识与学习
10.5.1 基于解释的学习
10.5.2 类比推理
10.6 无监督的学习
10.6.1 发现与无监督的学习
10.6.2 概念聚类
10.6.3 cobweb与分类知识的结构
10.7 增强式学习
10.7.1 增强式学习的成分
10.7.2 示例：井字棋博弈
10.7.3 增强式学习的推理算法
练习

第11章神经网络
11.1 神经网络基础
11.2 感知器学习
11.2.1 感知器训练算法
11.2.2 感知器学习用于分类
11.2.3 梯度下降法与δ规则
11.3 反向传播学习
11.3.1 反向传播算法
11.3.2 例1：nettalk
11.3.3 例2：异或函数
11.4 竞争学习
11.4.1 分类的wta学习算法
11.4.2 kohonen的学习原型网络
11.4.3 重复传播网络
11.5 hebb的叠合学习
11.5.1 无监督hebb学习示例
11.5.2 有监督hebb学习
11.5.3 结合存储与线性结合器
11.6 吸引状态网络
11.6.1 双向结合存储
11.6.2 bam处理示例
11.6.3 自结合存储与hopfield网络
练习

第12章学习的遗传与浮现模型
12.1 遗传算法
12.1.1 遗传算法示例
12.1.2 遗传算法的评价
12.2 分类器系统与遗传程序设计
12.2.1 分类器系统
12.2.2 遗传程序设计
12.3 人工生命和基于社会的学习
12.3.1 “生命博弈”
12.3.2 进化规划
12.3.3 浮现的专题研究
练习

第13章自动推理
13.1 通用问题求解器
13.2 归结定理证明器
13.2.1 谓词演算表达式化为短句集合
13.2.2 归结证明程序
13.2.3 归结策略和简化技术
13.2.4 由归结反驳抽取答案
13.3 prolog与自动推理
练习

第14章自然语言理解
14.1 理解语言的符号途径
14.2 语法
14.2.1 上下文无关文法的规范与分析
14.3 跃迁网分析器与语义
14.3.1 跃迁网分析器
14.3.2 chomsky层次与上下文有关文法
14.3.3 语义：atn分析器
14.3.4 用atn组合语法和语义知识
14.4 语言理解的随机工具
14.4.1 语法分析的概率途径
14.4.2 概率上下文无关分析器
14.5 自然语言的应用
14.5.1 故事理解与回答问题
14.5.2 数据库前端
14.5.3 对web的信息抽取和摘要系统
练习

第15章结束语：评述与展望
15.1 对ai几种途径的评述
15.1.1 智能与物理符号系统假设
15.1.2 连接主义计算
15.1.3 agent、浮现与智能
15.1.4 概率模型和随机技术
15.2 现代认知科学
15.2.1 心理学的约束
15.2.2 认识论问题
15.3 ai：当前的挑战与未来方向

附录a 随机方法导论
附录b 随机方法的应用
参考文献

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