This new 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 robotics and machine learning are now included. Advanced topics cover neural nets, genetic algorithms, natural language processing, planning, and complex board games. A companion DVD is provided with resources, applications, and figures from the book. Numerous instructors’ resources are available upon adoption. FEATURES: Includes new chapters on robotics and machine learning and new sections on speech understanding and metaphor in NLP 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 DVD with resources, simulations, and figures from the book Provides numerous instructors’ resources, including: solutions to exercises, Microsoft PP slides, etc. Table of Contents Part 1: Introduction Chapter 1 Overview of Artificial Intelligence PART II: FUNDAMENTALS Chapter 2 Uninformed Search Chapter 3 Informed Search Chapter 4 Search Using Games Chapter 5 Logic in Artificial Intelligence Chapter 6 Knowledge Representation Chapter 7 Production Systems PART III: KNOWLEDGE-BASED SYSTEMS Chapter 8 Uncertainty in AI Chapter 9 Expert Systems Chapter 10 Machine Learning: Part I Inductive Learning with Decision Trees Chapter 11 Machine Learning : Part II Neural Networks Chapter 12 Search Inspired by Mother Nature PART IV: ADVANCED TOPICS Chapter 13 Natural Language
Copyrighted materials Copyright@2016MercuryLearningandInfomationRetrievedfromwww.knovel.com To the memory of my parents, Magdalena and vladimir Kopec Who set the stage for me Danny Kopec To the memory of my parents, Louis and connie lucci Both of whom always encouraged my education Stephen lucci Copyrighted materials Copyright@2016MercuryLearningandInfomationRetrievedfromwww.knovel.com LICENSE. DISCLAIMER OF LIABILITY AND LIMITED WARRANTY By purchasing or using this book(the "Work), you agree that this license grants per- mission to use the contents contained herein, but does not give you the right of ownership to any of the textual content in the book or ownership to any of the information or products This license does not permit uploading of the Work onto the internet or on a network(of any kind) without the written consent of the Publisher: Duplication or dissemi- nation of any text, code, simulations, images, etc. contained herein is limited to and subject to licensing terms for the respective products, and permission must be obtained from the Publisher or the owner of the content, etc, in order to reproduce or network any portion of the textual material (in any media) that is contained in the Work Mercury Learning And Information LLC (MLI'or"the Publisher")and anyone in- volved in the creation, writing, or production of the companion disc, accompanying algo- rithms, code, or computer programs("the software), and any accompanying Web site or software of the Work, cannot and do not warrant the performance or results that might be obtained by using the contents of the Work. The author, developers, and the Publisher have used their best efforts to insure the accuracy and functionality of the textual material and/or programs contained in this package; we, however, make no warranty of any kind, express or implied, regarding the performance of these contents or programs. The Work is sold"as is without warranty(except for defective materials used in manufacturing the book or due The author, developers, and the publisher of any accompanying content and anyone involved in the composition, production, and manufacturing of this work will not be liable for damages of any kind arising out of the use of (or the inability to use)the algorithms source code, computer programs, or textual material contained in this publication. This includes, but is not limited to, loss of revenue or profit, or other incidental, physical, or consequential damages arising out of the use of this Work The sole remedy in the event of a claim of any kind is expressly limited to replacement of the book, and only at the discretion of the Publisher. The use of"implied warranty and certain"exclusions,vary from state to state, and might not apply to the purchaser of this Copyrighted materials Copyright@2016MercuryLearningandInfomationRetrievedfromwww.knovel.com Preface In 2006 Professor James Moor of the Philosophy Department at Dartmouth College asked me to organize a computer games exhibit and competition at Al@ 50, a conference that celebrated the 50th Anniversary of the Dartmouth Summer Conference where John Mc Carthy coined the term Artificial Intelligence. A number of the original attendees of that Dartmouth Conference were able to attend Al @50 including the late John McCarthy, Marvin Minsky, the late Oliver Selfridge, and Ray solomonoff. Professor Lucci also attended al@ 50 and shortly thereafter we agreed to collaborate on an ai text Perspective and Needs Our view is that Al is comprised of peoPle, IdEas, Methods, machines, and OUtcomes First, it is people who make up Al. People have ideas and these ideas become methods. Those ideas can be represented by algorithms, heuristics, procedures or systems that are the backbone of computation; and finally we have the production of those machines (programs) which we can call outcomes. Every outcome can be measured for its value, effectiveness, efficiency, etc We have found that existing AI books are often lacking in one or more of these areas. Without people there is no Al. Hence we have decided that it is important to"showcase" the people who have made ai successful through the human interest boxes which are sprinkled throughout the text From people come the ideas and the development of the methods that we present over the seventeen chapters of this book. AI and computer science are relatively young fields, compared to other sciences such as mathematics, physics, chemistry and biology. Yet, AI is a discipline that is truly interdisciplinary, combining elements of many other fields. Machines/computers are the tools of AI researchers allowing them to experiment, learn, and improve methods for problem-solving that can be applied in many interesting domains that can be beneficial to humankind. And finally, not in he least, there are the outcomes, measurable as the results of applying al to various problems and disciplines that remind us that ai must also be accountable. In a number of places in our text you will find discussions of the distinction between"performanceand "competence. Both are needed as the field of ai matures and advances To date, as faculty members who have taught from and observed the development of al texts. we have found that most of the available texts sometimes fall short in one or more of the areas described above. The names and vast contributions of Turing, McCarthy, Minsky, Michie, xvi■ Preface McLelland, Feigenbaum, Shortliffe, Lenat, Newell and Simon, Brooks, and many others should be familiar to students. Yet, this is not a history book! We believe, however, that the discipline, as interesting and widespread as it is, with its infinite potential, should justifiably be colored with the fascinating ideas and work of the people who comprise it Furthermore, students need hands on experience with problem solving, i. e, students need to get their hands"dirty?"with the fundamentals of search techniques fully explained in Chapters 2 through 4, the methods of logic in Chapter 5, and the role of knowledge representation in Al Chapter 6). Chapter 7 sets the stage for learning about fuzzy logic( Chapter 8)and expert systems ( Chapter 9) Advanced methods such as neural networks and genetic algorithms are thoroughly presented in Chapters 1l and 12. And finally, advanced topics such as natural language processing, planning, robotics and advanced computer games are covered in Chapters 13, 14, 15 and 16 respectively Chapter 17, Reprise, summarizes where youve been on your journey with us through Al, with view to the future The presentation is enhanced with several hundred fully worked examples, as well as over 300 figures and images, many in color. Students will also benefit from the significant number of solutions to exercises that have been provided How to use This book This text contains more material than can comfortably be covered in a one semester(45 contact hours)course. The authors have taught the following courses using material that has led to the development of Al in the 21st Century. Note that at CUNY, graduate courses often meet for three hours per week for 15 weeks Is a first course in al (graduate or undergraduate): I A Brief History of Al: Uses and limitations of the subject. Application areas Chapter 1 6 contact hours II Search Methodologies: State Space. Graphs. Generate and Test. Backtracking, Greedy Search. Blind Search Methods-depth first search breadth first search and depth-first iterative deepening Chapter 2 3 hours Ill Informed Search: Heuristics, Hill Climbing, Beam Search, Best First Search, Branch and Bound based Search and a* Search: And/Or trees Chapter 3(Section 3.7.- Bidirectional Search is optional) 3 hours IV Search Using Games: Game Trees and Minimax Evaluation. Elementary two-person games tic-tac-toe and nim Minimax with Alpha-Beta Pruning Chapter 4( Section 4.5-Game Theory and The Iterated Prisoner's Dilemma is optional) 3 hours v Logic in Al: Propositional logic and Predicate Logic(FOPL); Unification and resolution in FOPL Converting a Predicate Expression to Clause Form Chapter 5 (Section 5.4-Other Logics is optional) 6 hours VI Knowledge Representation: Choices of representation; Semantic Nets, Frames, and Scripts Inheritance and Object-Oriented Programming. Production Systems; Agent Methods Chapter 6(Sections 6.10, 6.11- Association and More Recent Approaches are optional) 3 hours Preface VIl Production Systems: Architecture and Examples, Resolution Strategies. Conflict Resolution Strategies. State Space Search- Data Driven and goal Driven Approaches, Cellular Automata(CA). One-dimensional CA(Wolfram) and Two-Dimensional Ca and The Game of life (Conway, Chapter 7(Section 7.6 Stochastic Processes and Markov Chains are optional) 3 hours VIll Expert Systems (ES): An Introduction: Why ES? Characteristics and Architectures for ES; Knowledge Engineering, Knowledge Acquisition and Classic Expert Systems; Newer Systems Case-based Approaches. Chapter 9(Sections 9.6, 9.7 and 9.8 are optional). 3 hours IX Introduction to Neural Computing: Rudiments of Artificial Neural Networks and and The Perceptron Learning rule Chapter 11 Sections 1.0, 11.I and 11.3 only 3 hours X Introduction to Evolutionary Computing-Genetic Algorithms Chapter 12 Sections 12.0 and 12. 2(only)2 hours XI Automated Planning: The Problem, Planning as Search; Means Ends AnalysiS(GPS)STRIPS Various planning algorithms and methods. More Modern Systems: Nonlin, Graphplan, etc Chapter14 Sections14.0,14.1,14.3.1,14.3.12,14.4.12 hours XII Epilog: Accomplishments of the First 50 years of Al. Prospects for the Future-Where are we going Chapter 17 2 hours Midterm Exam 3 hours Final exam 3 hours Two-Three Programming Assignments(one in Prolog) One Term Paper As a second course in al (AI-2, usually at the graduate level). Originally it was taught as a course in Neural Computing Artificial neural networks(ANN) are often used for learninge.g, in the distinction between pattern classes, it therefore seemed natural to incorporate genetic algorithms (ga) into the curriculum. al systems are often required to justify their reasoning, especially characteristic of expert systems ANNS are not especially proficient in this capacity. Fuzzy logic was added to ANNS and fuzzy ANNs are often used in conjunction to remedy this deficiency Emergence, ant colony optimization, fractals, artificial life and evolutionary computation (beyond ga)found their way into this course as all of these perspectives are useful in solving difficult problems. Many refer to such a rubric as"natural computing"because Mother Nature provides inspiration for these approaches. A proposed syllabus for AI-2 Preliminaries: Elementary Concepts: Natural Computing, Al, A-Life, emergence, feedback, agent top-down Vs bottom-up development. Supplementary material may be used here 3 hours Search inspired by Mother Nature: Search and State Space Graphs. Hill Climbing and its drawbacks. Simulated Annealing, Genetic Algorithms and Genetic Programming. Tabu Search. Ant Colony Optimization XX口 Preface Chapter 2 Sections 2.0, 2.1, and 2.1.1 Chapter 3 Sections 3.0. 3 1, and 3. 2 Chapter 12 10-15 hours) Ill Neural Networks: Artificial neurons VS. their biological counterparts. McCulloch-Pitts Neurons. The Perceptron Learning rule and its limitations The Delta rule. backpropagation Analyzing patterns and some guidelines for training. Discrete Hopfield Networks. Application Areas. Introduction to Machine Learning Chapter 10 Chapter 11 18 Hours I Fuzzy Sets and Fuzzy Logic: Crisp Sets vs Fuzzy Sets. Membership functions. Fuzzy logic and fuzzy inference systems Chapter 8 Sections 8.0-8.3 3 Hours V Optional Topics: Selected from: Unsupervised Learning in ANN Artificial Life including Cellular Automata Fractals and Complexit mmunocomputing Quantum Computing 2+ Hours A 3-hour midterm and a 3-hour final are given. There are 5-6 programming assignments and a term paper. Some alternative courses could easily be designed from the 17 Chapters weve developed A first course could, for example include: Chapter 1 (Introduction/Overview) Chapter 2 (Uninformed Search), Chapter 3 (Intelligent Search), Chapter 4(Search Using Games), Chapter 5 (Logic), Chapter 6(Knowledge Representation) Chapter 7(Production Systems), and Chapter 9 (Expert Systems) A second course could consist of: Chapter 8(Fuzzy Logic)Chapter 10(Machine Learnin Part 1) Chapter 11(Neural Networks), Chapter 12(Search Inspired by Mother Nature)and then one or two of the special topics chapters from Chapter 13(Natural Language Processing), Chapter 14 (Planning), Chapter 15 (Robotics), and Chapter 16(Advanced Computer Games) A Special Topics Course on Expert Systems might run: Chapter 1(Introduction), Chapter 7 (Production Systems), Chapter 9(Expert Systems)"spiced-up"with Chapter 12(Search Inspired by Mother Nature)and some supplemental papers /readings Stephen Lucci's vast classroom experience teaching AI courses at City College, Brooklyn College, and other CUNY schools, has often been lauded by students. Danny Kopec has considerable research experience in Computer Chess(University of Edinburgh, Machine Intelligence Research Unit), Intelligent Tutoring Systems(University of Maine, 1988-1992) and Computer Science Education Software Engineering/ Medical Errors, Technological Mishaps and Problem Solving (Brooklyn College from 1991-present). The text represents a strong collaboration of our efforts You will occasionally hear two voices sharing their ideas and experience. The writing process itself has often been joint, listening and adjusting to each other for knowledge, opinion, and style A SharedⅤ ISIon Writing this book has not been an overnight process. We also believe that our approaches, to writing and development of material, while different in many respects are complementary Preface■ We believe that the composite work should provide a strong foundation for anyone interested in AI with sufficient opportunity to accrue knowledge, experience, and competence in the methods that define the field. We are fortunate that both the authors and the publisher, David Pallai, president and founder of mercury learning and Information, have shared the same goals and vision for this book. Fundamental to this effort has been the agreement that the book should be balanced with theory and applications, accurate, pedagogically sound, and reasonably priced. The process has required several years, but we are particularly appreciative to Mr. Pallai for seeing the potential in our book and bringing it to fruition We hope that you will enjoy and learn from our efforts Preface to the second edition Much time has passed since the publication of our first edition. Artificial Intelligence concepts methods, and systems are becoming more integrated into everyday activities. For example, at the time when our first edition was being developed, many automobiles were built with the capability to parallel park themselves; it has now become commonplace to build cars with collision avoidance systems. Technologies that were the fantasies of sci-fi enthusiasts, e.g. drones and robots, are now a reality; increasingly, they are becoming a part of our everyday existence. Phone apps, GPs systems, and social networks, which were just surfacing in the first decade of 21st Century, are now pervasive. They include, optimal traffic routing, health advising, and personal attendants for diverse aspects of our lives, each typically using some form of Al. Advances in Natural language Processing and Speech have dramatically changed how we interact with machines The Second Edition has a new Chapter 10(Machine Learning: Part I) with presentation and discussion of decision Trees for machine learning. Hence Chapter 10, combined with Chapters 11 (Neural Approaches) and Chapter 12(an introduction to genetic approaches) together provide a foundation for further study. Chapter 13(Natural Understanding) has a new Section(13. 10)which presents the theory, methods, and applications of speech understanding. A new section on Metaphor in NLP is also included. Chapter 15 offers an overview of the field of robotics, including recent applications. The end of this chapter, in conjunction with Chapter 17(Reprise) provides a glimpse of what the future may hold for us. New exercises have been added to many of the original chapters Instructor Resource Files(Available Upon Adoption) The instructor files contain the complete set of power Point slides, solutions, sample exams, high resolution color figures, and an instructor's guide with teaching suggestions Online resources Digital versions of the text and all of the instructor and student materials are available at www.authorcloudware.comVitalsourceandmanyothere-vendors.Electronicreviewanddesk copies are also available Stephen Lucci Danny Kopec The City college of New York Brooklyn College(The City University of New York) NYNY November, 2015 Copyrighted materials Copyright@2016MercuryLearningandInfomationRetrievedfromwww.knovel.com Contents Preface Acknowledgments XLll Credits 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 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 al 1. 3 Heuristics 12 1.3.1 The Diagonal of a Rectangular Solid: Solving a Simpler, But Related Problem12 1.3.2 The Water Jug Problem: Working backward 1.4 Identifying Problems Suitable for Al 1.5 Applications and methods 16 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 Syste 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
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