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计算机科学丛书·人工智能：计算agent基础

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计算机科学丛书·人工智能：计算agent基础

Name: 计算机科学丛书·人工智能：计算agent基础
Brand: Jingdong book
SKU: 3118212001
Price: 36.26 USD
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Content Description

《计算机科学丛书·人工智能：计算agent基础》有机地将理论与实践结合起来，既详细地介绍了各种人工智能理论，又提供了实现的思路和程序，这将有利于本科生或研究生在学习人工智能时把对人工智能的理性认识转化为对具体程序的感性认识，从而弥补大部分人工智能教材偏重于理论的缺陷。《计算机科学丛书·人工智能：计算agent基础》可供计算机科学或计算机工程、哲学、认知科学和心理学等相关学科的高年级本科生或研究生学习。

Author Description

普尔（David L.Poole），加拿大不列颠哥伦比亚大学计算机科学教授，计算智能实验室主任。2014-2015年，他是牛津大学Leverhulme Trust客座教授。他还是加拿大人工智能学会（CAIAC）2013年终身成就奖获得者，是国际人工智能促进协会（AAAI）和加拿大人工智能学会（CAIAC）Fellow。

麦克活思（Alan K.Mackworth），加拿大不列颠哥伦比亚大学计算机科学教授。他的研究兴趣是基于约束的人工智能及其应用，被称为约束满足、机器人足球、混合系统和基于约束的Agent等研究领域的先驱。他是国际人工智能联合会议（IJCAI）公司的总裁和理事，以及IJCAI执行委员会主席；同时还担任许多编委会和程序委员会委员。他是加拿大计算机智能研究会（CSCSI）主席，还担任国际人工智能促进协会（AAAI）主席。荣获的奖励包括：ITAC/NSERC杰出学术奖.Killam研究奖，《人工智能》杂志经典论文奖，CSCSI杰出服务奖，AAAI杰出服务奖，CAIAC终身成就奖等。他是AAAI和CAIAC的Fellow，加拿大前沿科学研究院和加拿大皇家学院院士。

Comments

★“本书采用现代的思想，清晰地介绍了使用合理的计算Agent和逻辑作为统一线程的人工智能领域，包含许多完全的算例、各种难度的习题集、基于专门设计的声明式语言AII_，og的编程作业、通过Alspacc小程序来补充表示的整合良好的在线支持。如果你计划为本科或以上的学生教授一门人工智能的课程，必须认真对待这本非常有意思的书。”
——马可·瓦尔托尔塔，南卡罗业纳大学

★“本书的清晰度是令人惊讶的！每一章的材料都完美地综合考虑了以下方面：针对初学者的易理解性；针对高年级学生的理论性和挑战性；针对专家的可参考性。这些材料组织成不同的章节，教师可以针对学生做出适当的分割选择。这就好像是三本书合在一起！可以肯定地说，这是21世纪AI领域必备的一本书。”
——杰西·霍伊，邓迪大学

★“本书填补了Al文献的空白。它适用于高年级本科生，并且没有在技术严谨性上做出妥协。它是简洁的，但对AI的所有主要领域都给出了现代的表示，作为AI的介绍性课程是非常有用的。Poole和Mackworth就广泛、多样的人工智能领域的条理性做了不懈的努力。在这种条理性之下，所有的AJ都被放在由复杂性维度定义的智能Agent的设计空间里。”
——曼弗雷德·耶格，奥尔堡大学

Catalogue

出版者的话
译者序
前言
第一部分世界中的Agent：什么是Agent及如何创建它们
第1章人工智能与
1.1 什么是人工智能
1.2 人工智能简史
1.3 环境中的
1.4 知识表示
1.4.1 定义解
1.4.2 表示
1.4.3 推理与行为
1.5 复杂性维度
1.5.1 模块性
1.5.2 表示方案
1.5.3 规划期
1.5.4 不确定性
1.5.5 偏好
1.5.6 Agent数量
1.5.7 学习
1.5.8 计算限制
1.5.9 多维交互
1.6 原型应用
1.6.1 自主传送机器人
1.6.2 诊断助手
1.6.3 智能指导系统
1.6.4 交易Agent
1.7 本书概述
1.8 本章小结
1.9 参考文献及进一步阅读
1.1 0习题
第2章 Agent体系结构和分层控制
2.1 Agent
2.2 Agent系统
2.3 分层控制
2.4 嵌入式和仿真Agent
2.5 通过推理来行动
2.5.1 设计时间与离线计算
2.5.2 在线计算
2.6 本章小结
2.7 参考文献及进一步阅读
2.8 习题

第二部分表达和推理
第3章状态和搜索
3.1 用搜索进行问题求解
3.2 状态空间
3.3 图搜索
3.4 一个通用搜索算法
3.5 无信息搜索策略
3.5.1 深度优先搜索
3.5.2 宽度优先搜索
3.5.3 最低花费优先搜索
3.6 启发式搜索
3.6.1 A*搜索
3.6.2 搜索策略总结
3.7 更复杂的搜索方法
3.7.1 环检查
3.7.2 多路径剪枝
3.7.3 迭代深化
3.7.4 分支界限法
3.7.5 搜索方向
3.7.6 动态规划法
3.8 本章小结
3.9 参考文献及进一步阅读
3.1 0习题
第4章特征和约束
4.1 特征和状态
4.2 可能世界、变量和约束
4.2.1 约束
4.2.2 约束满足问题
4.3 生成测试算法
4.4 使用搜索求解CSP
4.5 一致性算法
4.6 域分割
4.7 变量消除
4.8 局部搜索
4.8.1 迭代最佳改进
4.8.2 随机算法
4.8.3 评估随机算法
4.8.4 局部搜索中利用命题结构
4.9 基于种群的方法
4.10 最优化
4.10.1 最优化的系统方法
4.10.2 局部搜索最优化
4.11 本章小结
4.12 参考文献及进一步阅读
4.13 习题
第5章命题和推理
5.1 命题
5.1.1 命题演算的语法
5.1.2 命题演算的语义
5.2 命题确定子句
5.2.1 问题与解答
5.2.2 验证
5.3 知识表示问题
5.3.1 背景知识与观察
5.3.2 询问用户
5.3.3 知识层的解释
5.3.4 知识层的调试
5.4 反证法验证
5.4.1 Horn子句
5.4.2 假说与冲突
5.4.3 基于一致性的诊断
5.4.4 通过假设和Horn子句推理
5.5 完备知识假设
5.5.1 非单调推理
5.5.2 完备知识的验证程序
5.6 溯因推理
5.7 因果模型
5.8 本章小结
5.9 参考文献及进一步阅读
5.10 习题
第6章不确定推理
6.1 概率
6.1.1 概率的语义
6.1.2 概率公理
6.1.3 条件概率
6.1.4 期望值
6.1.5 信息理论
6.2 独立性
6.3 信念网络
6.4 概率推理
6.4.1 信念网络中的变量消除
6.4.2 通过随机模拟进行近似推理
6.5 概率和时间
6.5.1 马尔可夫链
6.5.2 隐马尔可夫模型
6.5.3 监听和平滑算法
6.5.4 动态信念网络
6.5.5 时间粒度
6.6 本章小结
6.7 参考文献及进一步阅读
6.8 习题

第三部分学习与规划
第7章学习概述与有监督学习
7.1 学习问题
7.2 有监督学习
7.2.1 评估预测
7.2.2 无输入特征的点估计
7.2.3 概率学习
7.3 有监督学习的基本模型
7.3.1 决策树学习
7.3.2 线性回归与分类
7.3.3 贝叶斯分类器
……
第四部分个体与关系的推理
第五部分宏观图景

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