介绍针对期望最大化学习以及利用马尔可夫链蒙特卡罗采样的结构化学习的参数选择,加强学习中马尔可夫决策过程的利用。
介绍智能体技术和本体的使用。
介绍自然语言处理的动态规划(Earley语法析器),以及Viterbi等其他概率语法分析技术。
书中的许多算法采用Prolog.Lisp和Java语言来构建。
目录
Preface
Publisher's Acknowledgements
PART I ARTIFIClAL INTELLIGENCE:ITS ROOTS AND SCOPE
1 A1:HISTORY AND APPLICATIONS
1.1 From Eden to ENIAC:Attitudes toward Intelligence,Knowledge,andHuman Artifice
1.2 0verview ofAl Application Areas
1.3 Artificial Intelligence A Summary
1.4 Epilogue and References
1.5 Exercises
PART II ARTIFlClAL INTELLIGENCE AS REPRESENTATION AN D SEARCH
2 THE PREDICATE CALCULUS
2.0 Intr0血ction
2.1 The Propositional Calculus
2.2 The Predicate Calculus
2.3 Using Inference Rules to Produce Predicate Calculus Expressions
2.4 Application:A Logic—Based Financial Advisor
2.5 Epilogue and References
2.6 Exercises
3 STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH
3.0 Introducfion
3.1 GraphTheory
3.2 Strategies for State Space Search
3.3 using the state Space to Represent Reasoning with the Predicate Calculus
3.4 Epilogue and References
3.5 Exercises
4 HEURISTIC SEARCH
4.0 Introduction
4.l Hill Climbing and Dynamic Programmin9
4.2 The Best-First Search Algorithm
4.3 Admissibility,Monotonicity,and Informedness
4.4 Using Heuristics in Games
4.5 Complexity Issues
4.6 Epilogue and References
4.7 Exercises
5 STOCHASTIC METHODS
5.0 Introduction
5.1 The Elements ofCountin9
5.2 Elements ofProbabilityTheory
5.3 Applications ofthe Stochastic Methodology
5.4 Bayes’Theorem
5.5 Epilogue and References
5.6 Exercises
6 coNTROL AND IMPLEMENTATION OF STATE SPACE SEARCH
6.0 Introduction l93
6.1 Recursion.Based Search
6.2 Production Systems
6.3 The Blackboard Architecture for Problem Solvin9
6.4 Epilogue and References
6.5 Exercises
PARTIII CAPTURING INTELLIGENCE:THE AI CHALLENGE
7 KNOWLEDGE REPRESENTATION
7.0 Issues in Knowledge Representation
7.1 A BriefHistory ofAI Representational Systems
……
8 STRONG METHOD PROBLEM SOLVING
9 REASONING IN UNCERTAIN SITUATIONS
PART Ⅳ MACHINE LEARNING
10 MACHINE LEARNING:SYMBOL-BASED
11 MACHINE LEARNING:CONNECTIONIST
12 MACHINE LEARNING:GENETIC AND EMERGENT
13 MACHINE LEARNING:PROBABILISTIC
PART Ⅴ ADVANCED TOPICS FOR AI PROBLEM SOLVING
14 AUTOMATED REASONING
15 UNDERSTANDING NATURAL LANGUAGE
PART Ⅵ EPILOGUE
16 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY
内容提要(当当)
本书是一本经典的人工智能教材,全面阐述了人工智能的基础理论,有效结合了求解智能问题的数据结构以及实现的算法,把人工智能的应用程序应用于实际环境中,并从社会和哲学、心理学以及神经生理学角度对人工智能进行了独特的讨论。
书评(卓越)
“在该领域里学生经常遇到许罗很难的概念,通过深刻的实例与简单明了的祝圈,该书清晰而准确垲阚述了这些概念。”
——Toseph Lewis,圣迭戈州立大学
“本书是人工智能课程的完美补充。它既给读者以历史的现点,又给幽所有莰术的宾用指南。这是一本必须要推荐的人工智能的田书。”
——-Pascal Rebreyend,瑞典达拉那大学
“该书的写作风格和全面的论述使它成为人工智能领域很有价值的文献。”
——Malachy Eaton,利默里克大学
书摘(卓越)
插图:
postconditions of each action are in.the column below it. For example, row 5 lists the pre-conditions for pickup(X) and Column 6 lists the postconditions (the add and delete lists) ofpickup(X). These postconditions are placed in the row of the action that uses them as pre-conditions, organizing them in a manner relevant to further actions. The triangle table'spurpose is to properly interleave the preconditions and postconditions of each of thesmaller actions that make up the larger goal. Thus, triangle tables address non-linearityissues in planning>|注册欢迎登陆本站，认识更多朋友，获得更多精彩内容推荐！