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Introduction to Artificial Intelligence(2nd) 无水印原版pdf.pdf

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    Cover
    Copyright
    Preface to the Second Edition
    Preface to the First Edition
    Contents
    1 Introduction
    1.1 What Is Artificial Intelligence?
    1.1.1 Brain Science and Problem Solving
    1.1.2 The Turing Test and Chatterbots
    1.2 The History of AI
    1.2.1 The First Beginnings
    1.2.2 Logic Solves (Almost) All Problems
    1.2.3 The New Connectionism
    1.2.4 Reasoning Under Uncertainty
    1.2.5 Distributed, Autonomous and Learning Agents
    1.2.6 AI Grows Up
    1.2.7 The AI Revolution
    1.3 AI and Society
    1.3.1 Does AI Destroy Jobs?
    1.3.2 AI and Transportation
    1.3.3 Service Robotics
    1.4 Agents
    1.5 Knowledge-Based Systems
    1.6 Exercises
    2 Propositional Logic
    2.1 Syntax
    2.2 Semantics
    2.3 Proof Systems
    2.4 Resolution
    2.5 Horn Clauses
    2.6 Computability and Complexity
    2.7 Applications and Limitations
    2.8 Exercises
    3 First-order Predicate Logic
    3.1 Syntax
    3.2 Semantics
    3.2.1 Equality
    3.3 Quantifiers and Normal Forms
    3.4 Proof Calculi
    3.5 Resolution
    3.5.1 Resolution Strategies
    3.5.2 Equality
    3.6 Automated Theorem Provers
    3.7 Mathematical Examples
    3.8 Applications
    3.9 Summary
    3.10 Exercises
    4 Limitations of Logic
    4.1 The Search Space Problem
    4.2 Decidability and Incompleteness
    4.3 The Flying Penguin
    4.4 Modeling Uncertainty
    4.5 Exercises
    5 Logic Programming with PROLOG
    5.1 PROLOG Systems and Implementations
    5.2 Simple Examples
    5.3 Execution Control and Procedural Elements
    5.4 Lists
    5.5 Self-modifying Programs
    5.6 A Planning Example
    5.7 Constraint Logic Programming
    5.8 Summary
    5.9 Exercises
    6 Search, Games and Problem Solving
    6.1 Introduction
    6.2 Uninformed Search
    6.2.1 Breadth-First Search
    6.2.2 Depth-First Search
    6.2.3 Iterative Deepening
    6.2.4 Comparison
    6.2.5 Cycle Check
    6.3 Heuristic Search
    6.3.1 Greedy Search
    6.3.2 A★-Search
    6.3.3 Route Planning with the A★ Search Algorithm
    6.3.4 IDA★-Search
    6.3.5 Empirical Comparison of the Search Algorithms
    6.3.6 Summary
    6.4 Games with Opponents
    6.4.1 Minimax Search
    6.4.2 Alpha-Beta-Pruning
    6.4.3 Non-deterministic Games
    6.5 Heuristic Evaluation Functions
    6.5.1 Learning of Heuristics
    6.6 State of the Art
    6.6.1 Chess
    6.6.2 Go
    6.7 Exercises
    7 Reasoning with Uncertainty
    7.1 Computing with Probabilities
    7.1.1 Conditional Probability
    7.2 The Principle of Maximum Entropy
    7.2.1 An Inference Rule for Probabilities
    7.2.2 Maximum Entropy Without Explicit Constraints
    7.2.3 Conditional Probability Versus Material Implication
    7.2.4 MaxEnt-Systems
    7.2.5 The Tweety Example
    7.3 Lexmed, an Expert System for Diagnosing Appendicitis
    7.3.1 Appendicitis Diagnosis with Formal Methods
    7.3.2 Hybrid Probabilistic Knowledge Base
    7.3.3 Application of Lexmed
    7.3.4 Function of Lexmed
    7.3.5 Risk Management Using the Cost Matrix
    7.3.6 Performance
    7.3.7 Application Areas and Experiences
    7.4 Reasoning with Bayesian Networks
    7.4.1 Independent Variables
    7.4.2 Graphical Representation of Knowledge as a Bayesian Network
    7.4.3 Conditional Independence
    7.4.4 Practical Application
    7.4.5 Software for Bayesian Networks
    7.4.6 Development of Bayesian Networks
    7.4.7 Semantics of Bayesian Networks
    7.5 Summary
    7.6 Exercises
    8 Machine Learning and Data Mining
    8.1 Data Analysis
    8.2 The Perceptron, a Linear Classifier
    8.2.1 The Learning Rule
    8.2.2 Optimization and Outlook
    8.3 The Nearest Neighbor Method
    8.3.1 Two Classes, Many Classes, Approximation
    8.3.2 Distance Is Relevant
    8.3.3 Computation Times
    8.3.4 Summary and Outlook
    8.3.5 Case-Based Reasoning
    8.4 Decision Tree Learning
    8.4.1 A Simple Example
    8.4.2 Entropy as a Metric for Information Content
    8.4.3 Information Gain
    8.4.4 Application of C4.5
    8.4.5 Learning of Appendicitis Diagnosis
    8.4.6 Continuous Attributes
    8.4.7 Pruning—Cutting the Tree
    8.4.8 Missing Values
    8.4.9 Summary
    8.5 Cross-Validation and Overfitting
    8.6 Learning of Bayesian Networks
    8.6.1 Learning the Network Structure
    8.7 The Naive Bayes Classifier
    8.7.1 Text Classification with Naive Bayes
    8.8 One-Class Learning
    8.8.1 Nearest Neighbor Data Description
    8.9 Clustering
    8.9.1 Distance Metrics
    8.9.2 k-Means and the EM Algorithm
    8.9.3 Hierarchical Clustering
    8.9.4 How is the Number of Clusters Determined?
    8.10 Data Mining in Practice
    8.10.1 The Data Mining Tool KNIME
    8.11 Summary
    8.12 Exercises
    8.12.1 Introduction
    8.12.2 The Perceptron
    8.12.3 Nearest Neighbor Method
    8.12.4 Decision Trees
    8.12.5 Learning of Bayesian Networks
    8.12.6 Clustering
    8.12.7 Data Mining
    9 Neural Networks
    9.1 From Biology to Simulation
    9.1.1 The Mathematical Model
    9.2 Hopfield Networks
    9.2.1 Application to a Pattern Recognition Example
    9.2.2 Analysis
    9.2.3 Summary and Outlook
    9.3 Neural Associative Memory
    9.3.1 Correlation Matrix Memory
    9.3.2 The Binary Hebb Rule
    9.3.3 A Spelling Correction Program
    9.4 Linear Networks with Minimal Errors
    9.4.1 Least Squares Method
    9.4.2 Application to the Appendicitis Data
    9.4.3 The Delta Rule
    9.4.4 Comparison to the Perceptron
    9.5 The Backpropagation Algorithm
    9.5.1 NETtalk: A Network Learns to Speak
    9.5.2 Learning of Heuristics for Theorem Provers
    9.5.3 Problems and Improvements
    9.6 Support Vector Machines
    9.7 Deep Learning
    9.7.1 Nature as Example
    9.7.2 Stacked Denoising Autoencoder
    9.7.3 Other Methods
    9.7.4 Systems and Implementations
    9.7.5 Applications of Deep Learning
    9.8 Creativity
    9.9 Applications of Neural Networks
    9.10 Summary and Outlook
    9.11 Exercises
    9.11.1 From Biology to Simulation
    9.11.2 Hopfield Networks
    9.11.3 Linear Networks with Minimal Errors
    9.11.4 Backpropagation
    9.11.5 Support Vector Machines
    10 Reinforcement Learning
    10.1 Introduction
    10.2 The Task
    10.3 Uninformed Combinatorial Search
    10.4 Value Iteration and Dynamic Programming
    10.5 A Learning Walking Robot and Its Simulation
    10.6 Q-Learning
    10.6.1 Q-Learning in a Nondeterministic Environment
    10.7 Exploration and Exploitation
    10.8 Approximation, Generalization and Convergence
    10.9 Applications
    10.10 AlphaGo, the Breakthrough in Go
    10.11 Curse of Dimensionality
    10.12 Summary and Outlook
    10.13 Exercises
    11 Solutions for the Exercises
    11.1 Introduction
    11.2 Propositional Logic
    11.3 First-Order Predicate Logic
    11.4 Limitations of Logic
    11.5 PROLOG
    11.6 Search, Games and Problem Solving
    11.7 Reasoning with Uncertainty
    11.8 Machine Learning and Data Mining
    11.9 Neural Networks
    11.10 Reinforcement Learning
    References
    Index
    Undergraduate Topics in Computer Science Wolfgang Ertel Introduction to Artificial Intelligence Second Edition
    Undergraduate Topics in Computer Science Series editor Ian Mackie Advisory Board Samson Abramsky, University of Oxford, Oxford, UK Karin Breitman, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil Chris Hankin, Imperial College London, London, UK Dexter Kozen, Cornell University, Ithaca, USA Andrew Pitts, University of Cambridge, Cambridge, UK Hanne Riis Nielson, Technical University of Denmark, Kongens Lyngby, Denmark Steven Skiena, Stony Brook University, Stony Brook, USA Iain Stewart, University of Durham, Durham, UK
    Undergraduate Topics in Computer Science (UTiCS) delivers high-quality instruc- tional content for undergraduates studying in all areas of computing and information science. From core foundational and theoretical material to final-year topics and applications, UTiCS books take a fresh, concise, and modern approach and are ideal for self-study or for a one- or two-semester course. The texts are all authored by established experts in their fields, reviewed by an international advisory board, and contain numerous examples and problems. Many include fully worked solutions. More information about this series at http://www.springer.com/series/7592
    Wolfgang Ertel Introduction to Artificial Intelligence Second Edition Translated by Nathanael Black With illustrations by Florian Mast 123
    Wolfgang Ertel Hochschule Ravensburg-Weingarten Weingarten Germany ISSN 1863-7310 Undergraduate Topics in Computer Science ISBN 978-3-319-58486-7 DOI 10.1007/978-3-319-58487-4 ISBN 978-3-319-58487-4 (eBook) ISSN 2197-1781 (electronic) Library of Congress Control Number: 2017943187 1st edition: © Springer-Verlag London Limited 2011 2nd edition: © Springer International Publishing AG 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. trademarks, service marks, etc. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
    Preface to the Second Edition After 60 years, Artificial Intelligence (AI) has now reached industry and the con- sciousness of the population. The impressive successes and new AI methods are now so relevant that they should be taught even in a basic course. In about 30 new pages, I report mainly on deep learning, a consistent further development of neural networks, which finally enables image processing systems to recognize almost any object in pixel images. Among other benefits, this lead to the first computer pro- gram that could beat one of the world’s best Go players. In the new section on Deep Learning, we must not leave out a short report about the fascinating new subarea of creativity. For the first time neural networks can creatively generate texts, music pieces, and even paintings in the style of the old masters. These achievements are based on many years of research on neural net- works and machine learning. Practical AI has developed into an engineering dis- cipline in which programs are developed in large industrial teams by experts from various specializations. Self-driving cars, service robots, and smart homes—which are all applications of AI—will greatly change our lives. However, in addition to great rays of hope, there will be a dark side. Though we live in a time of rapid technological progress, we have long since exceeded the limits of growth. We must therefore think about sustainability when implementing each new invention. In Chap. 1, I would like to give you some food for thought about this topic. Other new additions to the book include a section on performance evaluation of clustering algorithms and two practical examples explaining Bayes’ theorem and its relevance in everyday life. Finally, in a section on search algorithms, we analyze the cycle check, explain route planning for car navigation systems, and briefly intro- duce Monte Carlo Tree Search. All known errors have been corrected and updates have been made in many places. I would like to sincerely thank the readers who have given me feedback and all those who contributed to this new edition through proofreading and suggestions. v
    vi Preface to the Second Edition I would especially like to thank Adrian Batzill for the route planning measurements and graphs, as well as Nate Black, Nicole Dathe, Markus Schneider, Robin Leh- mann, Ankita Agrawal, Wenzel Massag, Lars Berge, Jonas Lang, and Richard Cubek. Ravensburg March 2017 Wolfgang Ertel
    Preface to the First Edition Artificial Intelligence (AI) has the definite goal of understanding intelligence and building intelligent systems. However, the methods and formalisms used on the way to this goal are not firmly set, which has resulted in AI consisting of a multitude of subdisciplines today. The difficulty in an introductory AI course lies in conveying as many branches as possible without losing too much depth and precision. Russell and Norvig’s book [RN10] is more or less the standard introduction into AI. However, since this book has 1,152 pages, and since it is too extensive and costly for most students, the requirements for writing this book were clear: it should be an accessible introduction to modern AI for self-study or as the foundation of a four-hour lecture, with at most 300 pages. The result is in front of you. In the space of 300 pages, a field as extensive as AI cannot be fully covered. To avoid turning the book into a table of contents, I have attempted to go into some depth and to introduce concrete algorithms and applications in each of the following branches: agents, logic, search, reasoning with uncertainty, machine learning, and neural networks. The fields of image processing, fuzzy logic, and natural language processing are not covered in detail. The field of image processing, which is important for all of computer science, is a stand-alone discipline with very good textbooks, such as [GW08]. Natural language processing has a similar status. In recognizing and generating text and spoken language, methods from logic, probabilistic reasoning, and neural networks are applied. In this sense this field is part of AI. On the other hand, computer linguistics is its own extensive branch of computer science and has much in common with formal languages. In this book we will point to such appropriate systems in several places, but not give a systematic introduction. For a first introduction in this field, we refer to Chaps. 22 and 23 in [RN10]. Fuzzy logic, or fuzzy set theory, has developed into a branch of control theory due to its primary application in automation technology and is covered in the corresponding books and lectures. Therefore we will forego an introduction here. The dependencies between chapters of the book are coarsely sketched in the graph shown below. To keep it simple, Chap. 1, with the fundamental introduction for all further chapters, is left out. As an example, the thicker arrow from 2 to 3 means that propositional logic is a prerequisite for understanding predicate logic. vii
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