(Ebook) Artificial Intelligence in the 21st Century 1st Edition by Stephen Lucci, Sarhan M Musa, Danny Kopec ISBN 9781683922230 1683922239

(Ebook) Artificial Intelligence in the 21st Century 1st Edition by Stephen Lucci, Sarhan M Musa, Danny Kopec ISBN 9781683922230 1683922239

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ISBN 10: 1683922239
ISBN 13: 9781683922230
Author: Stephen Lucci, Sarhan M Musa, Danny Kopec

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.The companion files are available online by emailing the publisher with proof of purchase at [email protected].

(Ebook) Artificial Intelligence in the 21st Century 1st Edition Table of contents:

Part 1: 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 (The appendices are located on the companion files)

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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Tags: Stephen Lucci, Sarhan M Musa, Danny Kopec, Artificial Intelligence, 21st Century