Apress.Beginning.Artificial.Intelligence.with.the.Raspberry.Pi.2017

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Apress.Beginning.Artificial.Intelligence.with.the.Raspberry.Pi.2017
Beginning Artificial Intelligence with the raspberry pi Donald j. norris Barrington, New Hampshire USa ISBN-13(pbk):978-1-4842-2742-8 SBN-13( electronic:978-1-4842-2743-5 DOI10.1007/978-1-48422743-5 Library of Congress Control Number: 2017943462 Copyright o 2017 by Donald J. Norris 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 ofillustrations, 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 Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein Cover image designed by freepik Managing Director: Welmoed Spahr Editorial Director: Todd green Acquisitions Editor: Aaron Black Development Editor: Jim Markham Technical reviewer: Massimo nardone Coordinating Editor: Jessica Vakili Distributed to the book trade worldwide by springer Science+ Business Media New York 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax(201) 348-4505,e-mailorders-ny@springer-sbm.comorvisitwww.springeronline.com.Apress Media, LLC is a California LLC and the sole member (owner )is Springer Science Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation Forinformationontranslations,pleasee-mailrights@apress.com,orvisit http://www.apress.com/rights-permissions Apress titles may be purchased in bulk for academic, corporate, or promotional use eBook versions and licenses are also available for most titles For more information reference our PrintandeboOkBulkSaleswebpageathttp://www.apress.com/bulk-sales Any source code or other supplementary material referenced by the author in this book is available to readers on gitHub via the book's product page, located at Www.apress.com/978-1-4842-2742-8.Formoredetailedinformationpleasevisit http://www.apress.com/source-code Printed on acid-free paper This book is dedicated to my good friend and colleague, Dr. Lundy lewis. Dr Lewis and i both teach in the information Technology and computer Science Department at Southern Neu Hampshire University, located in Manchester, NH Lundy is a recognized expert in the field of artificial intelligence and robotics, often sought after to present talks on those subjects at international conferences and universities. He has been both a mentor and advisor to me in the exciting field of artificial intelligence Contents at a glance About the author About the technical reviewer Preface ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ ■■■■■■■■ XIX Chapter 1: Introduction to Artificial Intelligence n Chapter 2: Basic Al Concepts ma. ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■口■■ 17 Chapter3 Expert System Demonstrations,…,…,…,,,49 Chapter 4: Games ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ Chapter 5: Fuzzy Logic System mmammmmmammmmmmmmmmm 111 Chapter6: Machine Learning……,,,,m,,,,,…145 Chapter7: Machine Learning: Artificial Neural Networks…,……,171 Chapter 8: Machine Learning: Deep Learning ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 211 Chapter 9: Machine Learning: Practical ANN Demonstrations .. 249 ■ Chapter10:Evo| utionary Computing,,,,……281 Chapter 11: Behavior-Based robotics ammmmmmmammmmmmmmamam 313 Appendix A: Build Instructions for the Alfie robot car nmam 347 ndex ■■■■■ 363 Contents About the author About the technical reviewer Preface… ■■■■■■■■ XIX Chapter 1: Introduction to Artificial Intelligence n Al Historical Origins Intelligence ■■■■ Strong Al vs. Weak al, Broad al vs. Narrow a|.……… Reasoning… ,10 Al categories Al and big data ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 13 Summary......……15 Chapter 2: Basic Al Concepts at 17 Boolean Algebra Some Additional boolean laws 19 Inference 19 Expert Systems Conflict resolution 21 Backward chaining ................................................................................22 Raspberry Pi configuration 23 Introduction to SWI Prolog 23 CONTENTS Installing Prolog on a Raspberry pi. Initial Prolog demonstration.ameaaaeaeaaee 25 Introduction to Fuzzy Logic ,27 Example of FL . Defuzzification.wwwwww.29 Problem Solving 29 Breadth-First Search Depth-First Search Depth-Limited Search Bidirectional search,,,,………… 0031 Other Problem-Solving Examples........... Machine Learning… 31 C| assification………… 237 Prediction∴.32 Further classification.,,……… Neural networks 39 Shallow Learning VS. Deep Learning 6 Evolutionary Computing aaa eaeaeaeaeata 46 Genetic Algorithms….…………47 Summary 48 Chapter3: Expert System Demonstrati0ns,…,…,…,…,,…,49 Demo 3-1: office Database 50 Demo 3-2: Animal Demo 3-3: tic-tac-toe …62 Demo 3-4: Cold or Flu diagnosis .67 VIll CONTENTS Demo 3-5: Expert System with Raspberry pi GPIO Control stalling PySWIP…..…… 71 Hardware setup Rpi. GPIO Setup..................... 74 Expert System with LED Control Summary 6 Chapter 4: Games mmmmmaat 77 Demo 4-1: Rock-Paper-Scissors Rock-Paper-Scissors Game with Switches and lEDs Interrupts 86 Demo 4-2: nim 89 Nim with lCd and switches 97 LCD Display...........100 Loading the Adafruit LCD Library LCD Test……………10 automated nim. py. 103 Summary 110 Chapter5: Fuzzy Logic System,,,…,,…,,,,…,…,111 Parts list…11 Software installation 111 Basic Fls 112 Initialization: Define Linguistic Variables and Terms 113 Demo 5-1: Using FL to calculate a Tip 113 Initialization Construct Membership Functions. m.... 114 Membership Function visualization.. Initialization build rule set 118 Inference: Evaluate Fuzzy Set According to Rule Set 120 CONTENTS Aggregation: Combine Results from Each Rule Evaluation 123 Defuzzification: Convert Fuzzy Set to crisp Output Values 123 Demo 5-2: Modifications to the tipping. py Program 131 Demo 5-3: FLS Heating and cooling System 132 Fuzzificationwwww.135 Inference. mmm. 136 Aggregation.… Defuzzification Testing the Control program....,…,……………139 Demo 5-4: Modifications to the HVAC Program 141 Summary . Chapter 6: Machine Learning 145 Parts list 145 Demo 6-1: Color selection 146 Algorithm 146 Roulette Wheel Algorithm ...........................................................................149 Demo 6-2: autonomous robot 151 Autonomous algorithn.…...,,,……………………,……………153 Test run Additional Learning.……………160 Demo 6-3: Adaptive Learning with an Energy Consumption Consideration 164 Test run.……169 Summary.............,169 Chapter 7: Machine Learning: Artificial Neural Networks 171 Parts list 171 Hopfield Network 172 Demo 7-1: Numerical Figure Recognition Demonstration 179 CONTENTS Demo 7-2: Autonomous Robot Car Using aNN.m...mnam 186 Demo 7-3: Python Control Script for the obstacle-Avoiding Robot car 190 Test run 195 Dem07-4: Light- Seeking Robot.…....,,…,…………195 The unknowns… Brain Mapping.….,.,…..,.….…..99 Light Intensity Sensor. 200 Python Control Script for the goal- Seeking Robot Car…...,…………,202 Test run.……… 207 Obstacle Avoidance and Light Seeking. .................................208 Summary…,,, 209 Chapter 8: Machine Learning: Deep Learning ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 21 Generalized ann.mmmm.. 211 Larger ANN 217 Back Propagation In Three-layer ANNs 221 Updating the Weighting Matrix 223 The gradient Descent applied to an ann 232 Matrⅸ Multiplications for Weight Change Determination………,…235 Worked-through EXample 235 ssues with aNn Learning. Initial Weight Selection......................... 237 Dem08-1: ANN Python Scripts…..,… 238 Initialization 239 Test run.m.mm. 241 Demo 8-2: Training an ANN 243 246 Summary… 247

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