Artificial Intelligence in the 21st Century, 3rd Edition
- Length: 850 pages
- Edition: 3
- Language: English
- Publisher: Mercury Learning and Information
- Publication Date: 2022-09-02
- ISBN-10: 1683922239
- ISBN-13: 9781683922230
- Sales Rank: #4480592 (See Top 100 Books)
This third edition provides a comprehensive, colorful, up-to-date, and accessible presentation of AI without sacrificing theoretical foundations. It includes numerous examples, applications, full color images, and human interest boxes to enhance student interest. New chapters on deep learning, AI security, and AI programming are included. Advanced topics cover neural nets, genetic algorithms, natural language processing, planning, and complex board games. A companion disc is provided with resources, applications, and figures from the book. Numerous instructors’ resources are available upon adoption.
Features:
– Includes new chapters on deep learning, AI security, and AI programming
– Provides a comprehensive, colorful, up to date, and accessible presentation of AI without sacrificing theoretical foundations
– Uses numerous examples, applications, full color images, and human interest boxes to enhance student interest
– Introduces important AI concepts e.g., robotics, use in video games, neural nets, machine learning, and more thorough practical applications
– Features over 300 figures and color images with worked problems detailing AI methods and solutions to selected exercises
– Includes companion files with resources, simulations, and figures from the book
– Provides numerous instructors’ resources, including: solutions to exercises, Microsoft PP slides, etc.
Cover Half-Title Title Copyright Dedication Contents Preface Acknowledgments (Three Editions) Credits for the 3rd Edition Part I: Introduction Chapter 1: Overview of Artificial Intelligence 1.0 Introduction 1.0.1 What is Artificial Intelligence? 1.0.2 What is Thinking? What is Intelligence? 1.1 The Turing Test 1.1.1 Definition of the Turing Test 1.1.2 Controversies and Criticisms of the Turing Test 1.2 Strong AI versus Weak AI 1.3 Heuristics 1.3.1 The Diagonal of a Rectangular Solid: Solving a Simpler, but Related Problem 1.3.2 The Water Jug Problem: Working Backward 1.4 Identifying Problems Suitable for AI 1.5 Applications and Methods 1.5.1 Search Algorithms and Puzzles 1.5.2 Two-Person Games 1.5.3 Automated Reasoning 1.5.4 Production Rules and Expert Systems 1.5.5 Cellular Automata 1.5.6 Neural Computation 1.5.7 Genetic Algorithms 1.5.8 Knowledge Representation 1.5.9 Uncertainty Reasoning 1.6 Early History of AI 1.6.1 Logicians and Logic Machines 1.7 Recent History of AI to the Present 1.7.1 Games 1.7.2 Expert Systems 1.7.3 Neural Computing 1.7.4 Evolutionary Computation 1.7.5 Natural Language Processing 1.7.6 Bioinformatics 1.8 AI in the New Millennium 1.9 Chapter Summary Part II: Fundamentals Chapter 2: Uninformed Search 2.0 Introduction: Search in Intelligent Systems 2.1 State-Space Graphs 2.1.1 The False Coin Problem 2.2 Generate-and-Test Paradigm 2.2.1 Backtracking 2.2.2 The Greedy Algorithm 2.2.3 The Traveling Salesperson Problem 2.3 Blind Search Algorithms 2.3.1 Depth First Search 2.3.2 Breadth First Search 2.4 Implementing and Comparing Blind Search Algorithms 2.4.1 Implementing a Depth First Search Solution 2.4.2 Implementing a Breadth First Search Solution 2.4.3 Measuring Problem-Solving Performance 2.4.4 Comparing dfs and bfs 2.5 Chapter Summary Chapter 3: Informed Search 3.0 Introduction 3.1 Heuristics 3.2 Informed Search Algorithms (Part I) – Finding Any Solution 3.2.1 Hill Climbing 3.2.2 Steepest-Ascent Hill Climbing 3.3 The Best-First Search 3.4 The Beam Search 3.5 Additional Metrics for Search Algorithms 3.6 Informed Search (Part 2) – Finding An Optimal Solution 3.6.1 Branch and Bound 3.6.2 Branch and Bound with Underestimates 3.6.3 Branch and Bound with Dynamic Programming 3.6.4 The A* Search 3.7 Informed Search (Part 3) – Advanced Search Algorithms 3.7.1 Constraint Satisfaction Search 3.7.2 AND/OR Trees 3.7.3 The Bidirectional Search 3.8 Chapter Summary Chapter 4: Search Using Games 4.0 Introduction 4.1 Game Trees and Minimax Evaluation 4.1.1 Heuristic Evaluation 4.1.2 Minimax Evaluation of Game Trees 4.2 Minimax with Alpha-Beta Pruning 4.3 Variations and Improvements to Minimax 4.3.1 Negamax Algorithm 4.3.2 Progressive Deepening 4.3.3 Heuristic Continuation and the Horizon Effect 4.4 Games of Chance and the Expectiminimax Algorithm 4.5 Game Theory 4.5.1 The Iterated Prisoner’s Dilemma 4.6 Chapter Summary Chapter 5: Logic in Artificial Intelligence 5.0 Introduction 5.1 Logic and Representation 5.2 Propositional Logic 5.2.1 Propositional Logic – Basics 5.2.2 Arguments in the Propositional Logic 5.2.3 Proving Arguments in the Propositional Logic Valid – A Second Approach 5.3 Predicate Logic – Introduction 5.3.1 Unification in the Predicate Logic 5.3.2 Resolution in the Predicate Logic 5.3.3 Converting a Predicate Expression to Clause Form 5.4 Several Other Logics 5.4.1 Second Order Logic 5.4.2 Non-Monotonic Logic 5.4.3 Fuzzy Logic 5.4.4 Modal Logic 5.5 Chapter Summary Chapter 6: Knowledge Representation 6.0 Introduction 6.1 Graphical Sketches and The Human Window 6.2 Graphs and The Bridges of Königsberg Problem 6.3 Representational Choices 6.4 Production Systems 6.5 Object Orientation 6.6 Frames 6.7 Scripts and the Conceptual Dependency System 6.8 Semantic Networks 6.9 Associations 6.10 More Recent Approaches 6.10.1 Concept Maps 6.10.2 Conceptual Graphs 6.10.3 Baecker’s Work 6.11 Agents: Intelligent or Otherwise 6.11.1 A Little Agent History 6.11.2 Contemporary Agents 6.11.3 The Semantic Web 6.11.4 The Future – According to IBM 6.11.5 Author’s Perspective 6.12 Chapter Summary Chapter 7: Production Systems 7.0 Introduction 7.1 Background 7.2 Basic Examples 7.3 The CarBuyer System 7.3.1 Advantages of Production Systems 7.4 Production Systems and Inference Methods 7.4.1 Conflict Resolution 7.4.2 Forward Chaining 7.4.3 Backward Chaining 7.5 Production Systems and Cellular Automata 7.6 Stochastic Processes and Markov Chains 7.7 Chapter Summary Part III: Knowledge-Based Systems Chapter 8: Uncertainty in AI 8.0 Introduction 8.1 Fuzzy Sets 8.2 Fuzzy Logic 8.3 Fuzzy Inferences 8.4 Probability Theory and Uncertainty 8.5 Chapter Summary Chapter 9: Expert Systems 9.0 Introduction 9.1 Background 9.1.1 Human and Machine Experts 9.2 Characteristics of Expert Systems 9.3 Knowledge Engineering 9.4 Knowledge Acquisition 9.5 Classic Expert Systems 9.5.1 Dendral 9.5.2 Mycin 9.5.3 Emycin 9.5.4 Prospector 9.5.5 Fuzzy Knowledge and Bayes’ Rule 9.6 Methods for Efficiency 9.6.1 Demon Rules 9.6.2 The Rete Algorithm 9.7 Case-Based Reasoning 9.8 Other Expert Systems 9.8.1 Systems for Improving Employment Matching 9.8.2 An Expert System for Vibration Fault Diagnosis 9.8.3 Automatic Dental Identification 9.8.4 More Expert Systems Employing Case-Based Reasoning 9.9 Chapter Summary Chapter 10: Machine Learning : Part I Neural Networks 10.0 Introduction 10.1 Machine Learning: A Brief Overview 10.2 The Role of Feedback in Machine Learning Systems 10.3 Inductive Learning 10.4 Learning with Decision Trees 10.5 Problems Suitable for Decision Trees 10.6 Entropy 10.7 Constructing a Decision Tree With ID3 10.8 Issues Remaining 10.9 Rudiments of Artificial Neural Networks 10.10 McCulloch-Pitts Network 10.11 The Perceptron Learning Rule 10.12 The Delta Rule 10.13 Backpropagation 10.14 Implementation Concerns 10.14.1 Pattern Analysis 10.14.2 Training Methodology 10.15 Discrete Hopfield Networks 10.16 Application Areas 10.17 Chapter Summary Chapter 11: Machine Learning : Part II Deep Learning 11.0 Introduction 11.1 Deep Learning Applications: A Brief Overview 11.2 Deep Learning Network Layers 11.3 Deep Learning Types 11.3.1 Multilayer Neural Network 11.3.2 Convolutional Neural Network (CNN) 11.3.3 Recurrent Neural Network (RNN) 11.3.4 Long Short-Term Memory Network (LSTM) 11.3.5 Recursive Neural Network (RvNN) 11.3.6 Stacked Autoencoders 11.3.7 Extreme Learning Machine (ELM) 11.4 Chapter Summary Chapter 12: Search Inspired by Mother Nature 12.0 Introduction 12.1 Simulated Annealing 12.2 Genetic Algorithms 12.3 Genetic Programming 12.4 Tabu Search 12.5 Ant Colony Optimization 12.6 Chapter Summary Part IV: Advanced Topics Chapter 13: Natural Language Understanding 13.0 Introduction 13.1 Overview: The Problems and Possibilities of Language 13.1.1 Ambiguity 13.2 History of Natural Language Processing (NLP) 13.2.1 Foundations (1940s and 1950s) 13.2.2 Symbolic vs. Stochastic Approaches (1957–1970) 13.2.3 The Four Paradigms: 1970–1983 13.2.4 Empiricism and Finite-State Models 13.2.5 The Field Comes Together: 1994–1999 13.2.6 The Rise of Machine Learning 13.3 Syntax and Formal Grammars 13.3.1 Types of Grammars 13.3.2 Syntactic Parsing: The CYK Algorithm 13.4 Semantic Analysis and Extended Grammars 13.4.1 Transformational Grammar 13.4.2 Systemic Grammar 13.4.3 Case Grammars 13.4.4 Semantic Grammars 13.4.5 Schank’s Systems 13.5 Statistical Methods in NLP 13.5.1 Statistical Parsing 13.5.2 Machine Translation (Revisited) and IBM’s Candide System 13.5.3 Word Sense Disambiguation 13.6 Probabilistic Models for Statistical NLP 13.6.1 Hidden Markov Models 13.6.2 The Viterbi Algorithm 13.7 Linguistic Data Collections for Statistical NLP 13.7.1 The Penn Treebank Project 13.7.2 WordNet 13.7.3 Models of Metaphor in NLP 13.8 Applications: Information Extraction and Question Answering Systems 13.8.1 Question Answering Systems 13.8.2 Information Extraction 13.9 Present and Future Research (According to Charniak) 13.10 Speech Understanding 13.10.1 Speech Understanding Techniques 13.11 Applications of Speech Understanding 13.11.1 Dragon’s NaturallySpeaking System and Windows’ Speech Recognition System 13.11.2 CISCO’s Voice System 13.12 Chapter Summary Chapter 14: Automated Planning 14.0 Introduction 14.1 The Problem of Planning 14.1.1 Planning Terminology 14.1.2 Examples of Planning Applications 14.2 A Brief History and a Famous Problem 14.2.1 The Frame Problem 14.3 Planning Methods 14.3.1 Planning as Search 14.3.2 Partially Ordered Planning 14.3.3 Hierarchical Planning 14.3.4 Case-Based Planning 14.3.5 A Potpourri of Planning Methods 14.4 Early Planning Systems 14.4.1 Strips 14.4.2 Noah 14.4.3 Nonlin 14.5 More Modern Planning Systems 14.5.1 O-Plan 14.5.2 Graphplan 14.5.3 A Potpourri of Planning Systems 14.5.4 A Planning Approach to Learning Systems 14.5.5 The SCI Box Automated Planner 14.6 Chapter Summary Part V: The Present and Future Chapter 15: Robotics 15.0 Introduction 15.1 History: Serving, Emulating, Enhancing, and Replacing Man 15.1.1 Robot Lore 15.1.2 Early Mechanical Robots 15.1.3 Robots in Film and Literature 15.1.4 Twentieth-Century Robots 15.2 Technical Issues 15.2.1 Robot Components 15.2.2 Locomotion 15.2.3 Path Planning for a Point Robot 15.2.4 Mobile Robot Kinematics 15.3 Applications: Robotics in the Twenty-First Century 15.4 Chapter Summary Chapter 16: Advanced Computer Games 16.0 Introduction 16.1 Checkers: From Samuel to Schaeffer 16.1.1 Heuristic Methods for Learning in the Game of Checkers 16.1.2 Rote Learning and Generalization 16.1.3 Signature Table Evaluations and Book Learning 16.1.4 World Championship Checkers with Schaeffer’s Chinook 16.1.5 Checkers is Solved 16.2 Chess: The Drosophila of AI 16.2.1 Historical Background of Computer Chess 16.2.2 Programming Methods 16.2.3 Beyond the Horizon 16.2.4 Deep Thought and Deep Blue against Grandmaster Competition: 1988–1995 16.3 Contributions of Computer Chess to Artificial Intelligence 16.3.1 Search in Machines 16.3.2 Search in Man vs. Machine 16.3.3 Heuristics, Knowledge, and Problem-Solving 16.3.4 Brute Force: Knowledge vs. Search; Performance vs. Competence 16.3.5 Endgame Databases and Parallelism 16.3.6 Author Contributions 16.4 Other Games 16.4.1 Othello 16.4.2 Backgammon 16.4.3 Bridge 16.4.4 Poker 16.5 Go: The New Drosophila of AI? 16.5.1 The Stars of Advanced Computer Games 16.6 Chapter Summary Chapter 17: Reprise 17.0 Introduction 17.1 Recapitulation—PART I 17.2 Prometheus Redux 17.3 Recapitulation—PART II: Present AI Accomplishments 17.4 IBM Watson-Jeopardy Challenge 17.5 AI in the 21st Century 17.6 Chapter Summary Part VI: Security and Programming (Optional) Chapter 18: Artificial Intelligence in Security (Optional) 18.0 Introduction 18.1 Internet Protocol Security (IPSec) 18.2 Security Association (SA) 18.3 Security Policies 18.3.1 Security Policy Database (SPD) 18.3.2 Security Association Selectors (SA Selectors) 18.3.3 Combining of Security Associations 18.3.4 IPSec Protocol Modes 18.3.5 Anti-Replay Window 18.4 Secure Electronic Transactions 18.4.1 Business Requirements of SET 18.5 Intruders 18.6 Intrusion Detection 18.6.1 Intrusion Detection Techniques 18.7 Malicious Programs 18.7.1 Different Phases in the Lifetime of a Virus 18.8 Anti-Virus Scanners 18.8.1 Different Generations of Anti-Virus Scanners 18.9 Worms 18.10 Firewalls 18.10.1 Firewall Characteristics 18.10.2 Firewall Techniques to Control Access 18.10.3 Types of Firewalls 18.11 Trusted Systems 18.12 Chapter Summary Chapter 19: Artificial Intelligence Programming Tools (Optional) 19.1 Programming in Logic (Prolog) 19.1.1 Differences between C/C++ and Prolog 19.1.2 How Does Prolog Work? 19.1.3 Milestones in Prolog Language Development 19.1.4 Clauses 19.1.5 Robinson’s Resolution Rule 19.1.6 Parts of a Prolog Program 19.1.7 Queries to a Database 19.1.8 How Does Prolog Solve a Query? 19.1.9 Compound Queries 19.1.10 The _ Variable 19.1.11 Recursion in Prolog 19.1.12 Data Structures in Prolog 19.1.13 Head and Tail of a List 19.1.14 Print all the Members of the List 19.1.15 Print the List in Reverse Order 19.1.16 Appending a List 19.1.17 Find Whether the Given Item is a Member of the List 19.1.18 Finding the Length of the List 19.1.19 Controlling Execution in Prolog 19.1.20 Cut Predicate 19.1.21 About Turbo Prolog 19.2 Python 19.2.1 Running Python 19.2.2 Pitfalls 19.2.3 Features of Python 19.2.4 Functions as Rst-Class Objects 19.2.5 Useful Libraries 19.2.6 Utilities 19.2.7 Testing Code 19.3 MATLAB 19.3.1 Getting Started and Windows of MATLAB 19.3.2 Using MATLAB in Calculations 19.3.3 Plotting 19.3.4 Symbolic Computation 19.3.5 MATLAB for Python Users Appendices Appendix A. Example with CLIPS: The Expert System Shell Appendix B. Implementation of the Viterbi Algorithm for Hidden Markov Chains Appendix C. The Amazing Walter Shawn Browne Appendix D. Applications and Data Appendix E. Solutions to Selected Odd Exercises Index
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