介紹針對(duì)期望最大化學(xué)習(xí)以及利用馬爾可夫鏈蒙特卡羅采樣的結(jié)構(gòu)化學(xué)習(xí)的參數(shù)選擇,加強(qiáng)學(xué)習(xí)中馬爾可夫決策過程的利用。
介紹智能體技術(shù)和本體的使用。
介紹自然語言處理的動(dòng)態(tài)規(guī)劃(Earley語法析器),以及Viterbi等其他概率語法分析技術(shù)。
書中的許多算法采用Prolog.Lisp和Java語言來構(gòu)建。
目錄
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
內(nèi)容提要(當(dāng)當(dāng))
本書是一本經(jīng)典的人工智能教材,全面闡述了人工智能的基礎(chǔ)理論,有效結(jié)合了求解智能問題的數(shù)據(jù)結(jié)構(gòu)以及實(shí)現(xiàn)的算法,把人工智能的應(yīng)用程序應(yīng)用于實(shí)際環(huán)境中,并從社會(huì)和哲學(xué)、心理學(xué)以及神經(jīng)生理學(xué)角度對(duì)人工智能進(jìn)行了獨(dú)特的討論。
書評(píng)(卓越)
“在該領(lǐng)域里學(xué)生經(jīng)常遇到許羅很難的概念,通過深刻的實(shí)例與簡單明了的祝圈,該書清晰而準(zhǔn)確塏闞述了這些概念。”
——Toseph Lewis,圣迭戈州立大學(xué)
“本書是人工智能課程的完美補(bǔ)充。它既給讀者以歷史的現(xiàn)點(diǎn),又給幽所有莰術(shù)的賓用指南。這是一本必須要推薦的人工智能的田書。”
——-Pascal Rebreyend,瑞典達(dá)拉那大學(xué)
“該書的寫作風(fēng)格和全面的論述使它成為人工智能領(lǐng)域很有價(jià)值的文獻(xiàn)?！?br>——Malachy Eaton,利默里克大學(xué)
書摘(卓越)
插圖:
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>|注冊(cè)歡迎登陸本站，認(rèn)識(shí)更多朋友，獲得更多精彩內(nèi)容推薦！