Artificial Intelligence Simplified

about the second edition:

the book introduces key ai concepts in an easy-to-read format with examples and illustrations. our goal is to keep our explanations as simple as possible. while we have included some algorithms, flowcharts, and technical details, you should be able to get an idea of basic ai concepts even if you don't go in-depth into those contents. if you are a beginner, you may want to skip the depth content in your first reading.

if you are a professional and wish to get an overview of ai, this book will provide you with some essential background to begin. if you are a robotics enthusiast wanting to understand the broader aspects of ai, you may find this book useful. if you are a student taking an ai course, you can use this as an introductory textbook to develop a solid understanding of the basic ai concepts. this book includes numerous bibliographical references and resources that you may find useful for delving into more profound aspects of ai.

our first edition was very well received all over the world. we received fascinating reviews and encouraging feedback from readers, professionals, and well-wishers. based on inputs received, we prepared this second edition, including more relevant topics such as generative adversarial network (gan), recurrent neural network (rnn), support vector machine (svm), and artificial vision. a few readers wanted more depth content. so we added more advanced material without severely compromising the simplicity. a beginner can skim over the depth content for your first reading.

Gail Carmichael is currently a technical educator at Shopify, where she led the design and launch of the work-integrated learning program Dev Degree. She previously worked as a full-time instructor at Carleton University, where she taught both majors and non-majors a variety of computer science courses. She is particularly passionate about teaching beginners and enticing them to fall in love with computer science, whether as a major or as a tool to help them in their own fields. She co-founded Carleton University's Women in Science and Engineering, helped launch the now Ontario wide Go Code Girl high school outreach program, and has developed and taught many computing workshops and courses for folks of all ages.
Dr. Binto George is a professor in the School of Computer Sciences at Western Illinois University (WIU), Macomb, IL, USA. Before joining WIU, he worked at Rutgers University. Dr. George received his Ph.D. from the Indian Institute of Science, Bangalore. He has authored several journal articles, conference papers, book chapters, and books. As the principal investigator, Dr. George has led the National Science Foundation (NSF) funded research to incorporate usable security into the computer science curriculum. He loves teaching and developing new courses. Dr. George is a partner of the CSTrends LLP, an organization committed to making Computer Science accessible for all. Dr. George is a member of the IEEE, IEEE Computer Science Society, and the Association for Computing Machinery (ACM). Dr. George actively participates in community service and curriculum development activities.

1. introduction 13

1.1. organization of this book 16

2. search methods for problem-solving 21

2.1. modeling operating room scheduling problem 22

2.2. generate and test 28

2.3. making search more efficient 31

2.4. blind search methods 40

2.5. heuristic search methods 52

2.5.1 hill climbing 54

2.5.2 best first search 68

2.6. best path methods 71

3. handling competing goals with game trees 79

3.1. minimax algorithm 83

3.2. horizon effect 85

3.3. alpha-beta pruning 85

3.4. progressive deepening 88

4. planning techniques 91

4.1. forward planning 93

4.2. backward planning 96

4.3. partial-order planning 97

4.4. planning under uncertainty 99

5. evolutionary computing 101

5.1. crossover 104

5.2. mutation 105

5.3. fitness function 108

5.4. genetic programming 108

6. expert systems 111

6.1. knowledge representation 112

6.2. expert systems 114

6.3. expert system types 115

6.3.1 forward chaining 115

6.3.2 backward chaining 116

6.3.3 hybrid chaining 118

6.3.4 deduction and reaction systems 118

6.3.5 explanation facility 119

6.4. inference under uncertainty 119

6.5. fuzzy expert systems 126

6.5.1 fuzzification 131

7. learning from experience 137

7.1. gradient descent training algorithm 147

7.2. regression and classification 150

7.3. multi-layer neural networks 155

7.3.1 backpropagation 156

7.4. convolutional neural network (cnn) 159

7.5. recurrent neural network (rnn) 163

7.6. generative adversarial network (gan) 164

7.7. support vector machine (svm) 166

7.8. bayesian neural networks (bnn) 168

7.9. applications of machine learning 169

7.9.1 ann deployment 170

8. human interaction and robotics 173

8.1. natural language understanding 178

8.2. speech synthesis 181

8.3. artificial vision 182

9. evaluating intelligence 185

9.1. identifying intelligence 188

9.2. partial intelligence 190

9.3. bringing up intelligence 193

10. conclusions and where to go from here 197

10.1. how is ai deployed today? 198

10.2. ai and other disciplines 200

10.3. the future of artificial intelligence 201

11. bibliography 211

index 233