Structure of expert systems. Department of Informatics and Information Technologies
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2 E. V. Borovskaya N. A. Davydova FUNDAMENTALS OF ARTIFICIAL INTELLIGENCE Textbook 3rd edition (electronic) Moscow Knowledge Laboratory 2016
3 UDC BBK B83 Series founded in 2007 Borovskaya E. V. B83 Fundamentals artificial intelligence [Electronic resource] : tutorial/ E. V. Borovskaya, N. A. Davydova. 3rd ed. (el.). Electron. text data (1 pdf file: 130 pp.). M.: Laboratory of knowledge, (Pedagogical education). System. requirements: Adobe Reader XI; screen 10". ISBN The textbook introduces readers to the history of artificial intelligence, knowledge representation models, expert systems and neural networks. The main directions and methods used in the analysis, development and implementation of intelligent systems are described. Knowledge representation models and methods of working with them are considered, methods for developing and creating expert systems. The book will help the reader master the skills of logical database design. subject area and programming in ProLog language. For students and teachers of pedagogical universities, teachers secondary schools, gymnasiums, lyceums. UDC BBK Derivative electronic publication based on the printed analogue: Fundamentals of artificial intelligence: textbook / E. V. Borovskaya, N. A. Davydova. M.: BINOM. Knowledge Laboratory, p. : ill. (Teacher Education). ISBN In accordance with Article 1301 of the Civil Code of the Russian Federation, when eliminating the restrictions established technical means copyright protection, the copyright holder has the right to demand damages or compensation from the infringer ISBN c Knowledge Laboratory, 2015
4 CONTENTS Chapter 1. Artificial intelligence Introduction to artificial intelligence systems The concept of artificial intelligence Artificial intelligence in Russia Functional structure artificial intelligence systems Directions for the development of artificial intelligence Data and knowledge. Representation of knowledge in intelligent systems Data and knowledge. Basic definitions Knowledge representation models Expert systems Structure of an expert system Development and use of expert systems Classification of expert systems Representation of knowledge in expert systems Tools for constructing expert systems Expert system development technology Control questions and assignments for the chapter Literature for the chapter Chapter 2. Logic programming Programming methodologies Imperative programming methodology Object-oriented programming methodology Methodology functional programming Logic programming methodology Constraint programming methodology Neural network programming methodology A brief introduction to predicate calculus and theorem proving The process of logical inference in Prolog... 58
5 4 Contents 2.4. Structure of a Prolog program Using composite objects Using alternative domains Organizing repetition in Prolog The rollback method after failure The cut and rollback method Simple recursion The generalized recursion rule (GRR) method Lists in the Prolog language Operations on lists Strings in the Prolog language Operations on strings Files in the language Prolog Prolog predicates for working with files Description of the file domain Write to a file Read from a file Modify existing file Adding to the end of an existing file Creating dynamic databases in the Prolog language Databases in Prolog Predicates of a dynamic database in the Prolog language Creating expert systems Structure of an expert system Knowledge representation Inference methods User interface system Rule-based expert system Test questions and assignments for the chapter Literature to chapter Chapter 3. Neural networks Introduction to neural networks Artificial model Neuron Application neural networks Training a neural network Test questions and assignments for the chapter Literature for the chapter
6 CHAPTER 1 ARTIFICIAL INTELLIGENCE 1.1. Introduction to artificial intelligence systems The concept of artificial intelligence An artificial intelligence (AI) system is a software system that simulates the human thinking process on a computer. To create such a system, it is necessary to study the very thinking process of a person solving certain problems or making decisions in specific area, highlight the main steps of this process and develop software, reproducing them on a computer. Therefore, AI methods involve a simple structured approach to the development of complex software systems decision making . Artificial intelligence is a branch of computer science, the goal of which is to develop hardware and software that allows a non-programmer user to set and solve their traditionally considered intellectual problems, communicating with a computer in a limited subset of natural language. The idea of creating an artificial likeness of a person to solve complex tasks and modeling of the human mind, as they say, “was in the air” back in ancient times. The founder of artificial intelligence is considered to be the medieval Spanish philosopher, mathematician and poet Raymond Lull, who back in the 13th century. tried to create mechanical device to solve various problems based on the universal classification of concepts he developed.
7 6 Chapter 1 Later, Leibniz and Descartes independently continued this idea by proposing universal languages classifications for all sciences. These works can be considered the first theoretical works in the field of artificial intelligence. However, the final birth of artificial intelligence as a scientific direction occurred only after the creation of computers in the 1940s, when Norbert Wiener created his seminal works on the new science of cybernetics. The term “artificial intelligence” (AI; English AI “Artificial Intelligence”) was proposed in 1956 at a seminar with the same name at Dartmouth College (USA). This seminar was devoted to the development of methods for solving logical (rather than computational) problems. Note that in English language this phrase does not have that slightly fantastic anthropomorphic connotation that it acquired in the rather unsuccessful Russian translation. The word “intelligence” simply means “the ability to reason intelligently,” and not “intelligence” at all (for which there is a separate English analogue “intellect”). Soon after artificial intelligence was recognized as a special field of science, it was divided into two areas: neurocybernetics and “black box” cybernetics. These areas developed almost independently, differing significantly both in methodology and technology. And only now have trends towards combining these parts again into a single whole become noticeable. Neurocybernetics The main idea of this direction can be formulated as follows: “ The only object, capable of thinking, is the human brain, so any thinking device must somehow reproduce its structure.” Thus, neurocybernetics is focused on software and hardware modeling of structures similar to the structure of the brain. Neurocybernetics efforts have focused on creating elements similar to neurons and integrating them into functioning systems, neural networks.
8 Artificial Intelligence 7 The first neural networks were created in the 1990s. They weren't very good successful attempts create systems that simulate the human eye and its interaction with the brain. Gradually in the 1920s The number of works in this area of artificial intelligence began to decline; the first results were too disappointing. Typically, the authors of the developments explained their failures small memory and the low performance of computers existing at that time. The first neurocomputer was created in Japan as part of the “Fifth Generation Computer” project. By this time, restrictions on computer memory and speed were practically lifted. Transputer computers appeared with a large number of processors that implement parallel computing. Transputer technology is one of a dozen new approaches to the hardware implementation of neural networks that model the hierarchical structure of the human brain. In general, today we can distinguish three main types of approaches to creating neural networks: hardware (creation of special computers, neurochips, expansion cards, chipsets), software (creation of programs and software tools designed for high-performance computers; such networks are created “virtually”, in memory computer, while all the work is done by its own processors) and hybrid (a combination of the first two methods). Black box cybernetics and artificial intelligence This approach is based on the principle opposite to neurocybernetics. Here it no longer matters how exactly the “thinking” device is structured, the main thing is that it reacts to the given input influences in the same way as the human brain. Proponents of this trend motivated their approach by the fact that people should not blindly follow nature in their scientific and technological searches. In addition, the border sciences about man were unable to contribute significantly
9 8 Chapter 1 of the th theoretical contribution that explains (at least approximately) how intellectual processes occur in a person, how his memory works and how a person perceives the world around him. This area of artificial intelligence was focused on finding algorithms for solving intellectual problems on existing computer models. Significant contributions to the formation of the new science were made by such pioneers as McCarthy, Minsky, Newell, Simon, Shaw, Hunt and others. In the 1990s. Intensive searches were carried out for models and algorithms of human thinking and the development of the first programs based on them. Representatives of the humanities, philosophers, psychologists, and linguists, neither then nor now, were able to propose such algorithms, then cybernetics began to create their own models. Thus, various approaches were consistently created and tested. At the end of the 1950s. a labyrinthine search model appeared. This approach represents the problem as a certain state space in the form of a graph, after which the optimal path from the input data to the resultant data is searched in this graph. has been done big job to create such a model, but to solve practical problems this idea still has not found widespread use. Early 1960s became the era of heuristic programming. A heuristic is a rule that is not theoretically justified, but allows you to reduce the number of searches in the search space. Heuristic programming is the development of an action strategy based on known, predetermined heuristics. In the 1990s Methods of mathematical logic began to be used to solve problems. Robinson developed the resolution method, which allows one to automatically prove theorems given a set of initial axioms. Around the same time, the outstanding Russian mathematician Yu. S. Maslov proposed the so-called inverse derivation (later named after him), solving a similar problem in a different way. Based on resolution method
10 Artificial Intelligence 9 Frenchman Albert Colmeroe created the logic programming language Prolog in 1973. The “Theoretical Logician” program, created by Newell, Simon and Shaw, which proved school theorems, caused a great stir in the scientific community. However, most real problems cannot be reduced to a set of axioms, and a person, solving production tasks, does not always use classical logic, so logic models, despite all their advantages, have significant limitations on the classes of problems they solve. The history of artificial intelligence is full of dramatic events, one of which was the so-called “Lighthill report” in 1973, which was prepared in Great Britain by order of the British Research Council. The famous mathematician Lighthill, who is not professionally associated with artificial intelligence, prepared a review of the state of affairs in this area. The report acknowledged some achievements, but rated them as “disappointing” and the overall assessment was negative in terms of practical significance. This report set European researchers back about five years, as funding for the work was significantly reduced. Around the same time, a significant breakthrough in the development practical applications artificial intelligence originated in the United States in the mid-1970s. The search for a universal thinking algorithm was replaced by the idea of modeling the specific knowledge of specialist experts. The first commercial knowledge-based systems, or expert systems (ES), appeared in the United States. Began to be used new approach knowledge representation for solving problems of artificial intelligence. The first two expert systems for medicine and chemistry, Mycin and Dendral, were created, which became classics. The Pentagon also made a significant financial contribution, proposing to base the new US Department of Defense program on the principles of artificial intelligence. Already catching up with missed opportunities, the European Union in the early 1980s. announced global program development of new ESPRIT technologies,
11 10 Chapter 1, which included the problems of artificial intelligence. At the end of the 1970s. Japan is joining the race, announcing the start of a fifth-generation knowledge-based machine project. The project was designed for ten years and brought together the best young specialists from the largest Japanese computer corporations. A new institute, ICOT, was specially created for these specialists and given complete freedom of action (though without the right to publish preliminary results). As a result, they created a rather cumbersome and expensive symbolic processor that implemented a Prolog-like language in software, but did not receive wide acceptance. However, the positive effect of this project was obvious. In Japan, a significant group of highly qualified specialists in the field of artificial intelligence has emerged, which has achieved significant results in various applied problems. By the mid-1990s. The Japanese Association of Artificial Intelligence already numbered 40 thousand people. Since the mid-1980s. The commercialization of artificial intelligence was taking place everywhere. Annual capital investments grew, and industrial expert systems were created. There was growing interest in self-learning systems. Dozens of scientific journals were published, international and national conferences were held annually in various areas of artificial intelligence, which was becoming one of the most promising and prestigious areas of computer science. Currently, there are two main approaches to modeling artificial intelligence: machine intelligence, which consists in strictly specifying the result of operation, and artificial intelligence, aimed at modeling the internal structure of the system. Modeling of systems of the first group is achieved through the use of the laws of formal logic, set theory, graphs, semantic networks and other achievements of science in the field of discrete computing, and the main results are the creation of expert systems, parsing systems
12 Artificial intelligence 11 natural language and simple control systems of the “stimulus-response” type. The systems of the second group are based on the mathematical interpretation of the activity of the nervous system (primarily the human brain) and are implemented in the form of neuron-like networks based on a neuron-like element analogous to a neuron Artificial intelligence in Russia In 1954, the seminar “Automata and Thinking” began at Moscow State University under the guidance of an academician A. A. Lyapunov (), one of the founders of Russian cybernetics. This seminar was attended by physiologists, linguists, psychologists, and mathematicians. It is generally accepted that it was at this time that artificial intelligence was born in Russia. As well as abroad, it has two main directions: neurocybernetics and “black box” cybernetics. In the 1990s were created individual programs and research was carried out in the field of finding solutions to logical problems. At LOMI (Leningrad branch of the Steklov Mathematical Institute) the ALPEV LOMI program was created, which automatically proves theorems, which is based on the original reverse output Maslov, similar to Robinson’s resolution method. Among the most significant results obtained by domestic scientists in the 1960s, it should be noted the “Kora” algorithm by M. M. Bongard, which models the activity human brain in pattern recognition. Such outstanding scientists as M. L. Tsetlin, V. N. Pushkin, M. A. Gavrilov, whose students became pioneers of this science in Russia, also made a great contribution to the development of the Russian school of artificial intelligence. In the 1990s a new direction of situational AI was born (corresponding to the representation of knowledge in Western terminology). The founder of this scientific school was Professor D. A. Pospelov. Special models for presenting situations (representing knowledge) were also developed. Despite the fact that the attitude towards new sciences in Soviet Russia has always been wary, science with such a “challenging
13 12 Chapter 1 with a new title also did not escape this fate and was met with hostility at the Academy of Sciences. Fortunately, among the members of the USSR Academy of Sciences there were people who were not afraid of such an unusual phrase as the name of a new scientific direction. However, only in 1974, under the Committee on System Analysis under the Presidium of the USSR Academy of Sciences, a scientific council on the problem of “Artificial Intelligence” was created, headed by D. A. Pospelov. On the initiative of this council, five complex scientific projects were organized, headed by leading experts in this field: “Dialogue” (work on natural language understanding), “Situation” (situational management), “Bank” (data banks), “Constructor” (search engine) design) and “Robot intelligence”. In the 1990s were held in our country active research in the field of knowledge representation, knowledge representation languages and expert systems were developed; The Refal language was created at Moscow State University. In 1988, the Association of Artificial Intelligence (AI) was formed, and D. A. Pospelov was unanimously elected president. Within the framework of this association, it was carried out a large number of research, schools for young specialists, seminars, symposiums were organized, joint conferences were held every two years, and a scientific journal was published. It should be noted that the level of theoretical research on artificial intelligence in Russia has always been no lower than the global level. However, unfortunately, since the 1980s. The gradual lag in technology began to affect applied work. On this moment The lag in the development of industrial intelligent systems is approximately 3-5 years. The main areas of application of AI systems: theorem proving, games, pattern recognition, decision making, adaptive programming, composing machine music, natural language processing, learning networks (neural networks), verbal conceptual learning.
14 Artificial intelligence Functional structure of an artificial intelligence system The functional structure of an AI system (Fig. 1.1) consists of three complexes of computing tools. The first of them is an executive system (IS), a set of tools that execute programs and are designed in terms of effective solution tasks; this complex has, in a number of cases, a problematic orientation. The second complex is a set of intelligent interface tools that have flexible structure, which provides the ability to adapt to a wide range of interests end users. The third set of tools with the help of which the interaction of the first two complexes is organized is the knowledge base, which ensures the use of computing tools by the first Fig. Functional structure of the AI system
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16 Minimum system requirements are determined by the relevant requirements Adobe programs Reader version not lower than 11th for platforms Windows, Mac OS, Android, ios, Windows Phone and BlackBerry; screen 10" Educational electronic publication Series: "Pedagogical education" FUNDAMENTALS OF ARTIFICIAL INTELLIGENCE Textbook Leading editor D. Usenkov Artists N. Novak, S. Infante Technical editor E. Denyukova Proofreader L. Makarova Computer layout: S. Yankovaya Signed for use Format mm Publishing house "Laboratory of Knowledge", Moscow, Aeroporta proezd, 3 Telephone: (499)
17 BOROVSKAYA ELENA VLADIMIROVNA Senior Lecturer at the Department of Informatics and Methods of Teaching Informatics, Chelyabinsk State Pedagogical University. Area of interest: problems of a modular rating system for monitoring and assessing students' educational achievements in the context of quality management at a university. DAVYDOVA NADEZHDA ALEKSEEVNA Candidate of Pedagogical Sciences, majoring in “Theory and Methods of Teaching and Education (Informatics, General Education Level)”, Associate Professor of the Department of Informatics and Methods of Teaching Informatics, Chelyabinsk State Pedagogical University. Areas of interest: technology for forming the content of computer science education in specialized classes of secondary schools, intelligent teaching systems. The textbook introduces readers to the history of artificial intelligence, knowledge representation models, expert systems and neural networks. The main directions and methods used in the analysis, development and implementation of intelligent systems are described. Models of knowledge representation and methods of working with them, methods of developing and creating expert systems are considered. The book will help the reader master the skills of logical design of domain databases and programming in Prolog. The book is intended for students and teachers of pedagogical universities, teachers of secondary schools, gymnasiums and lyceums.
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Kolomna Institute (branch)
State educational institution of higher education
vocational education
"MOSCOW STATE OPEN UNIVERSITY"
Department of Informatics and Information Technologies
"APPROVED"
Educational and methodological
Council of KI (f) MGOU
Chairman of the board
Professor
A.M. Lipatov
"___" ____________ 2010
P.S. Romanov
BASICS OF ARTIFICIAL INTELLIGENCE
Textbook for disciplines of the direction
"Informatics and Computer Science"
For higher education students educational institutions
Kolomna – 2010
U
Published in accordance with the decision of the educational and methodological council of the Kolomna Institute (branch) of the State Educational Institution of Higher Professional Education "MGOU" dated __________
2010
City No. ________
P69 Romanov P.S.
Fundamentals of artificial intelligence. Tutorial. – Kolomna: KI (f) MGOU, 2010. – 164 p.
The tutorial covers the basics of artificial intelligence. The basic concepts of artificial intelligence are presented. The provisions of the theory of fuzzy sets are given. The main intelligent systems, their purpose, classification, characteristics, problems of creation, examples are considered.
The textbook is intended for students of higher educational institutions studying in the field of “Informatics and Computer Science”. Can be used when studying intelligent information systems by students of other specialties.
Reviewer: Doctor of Technical Sciences, Professor V.G. Novikov
©Romanov P.S.
©KI(f) MGOU, 2010
Introduction…………………………………………………………………………………………...5
Chapter 1. Basic concepts of artificial intelligence...................................6
§ 1.1. Basic terms and definitions............................................................. .....6
§ 1.2. History of the development of AI systems................................................................. .............12
§ 1.4. Main directions of development and application
intelligent systems........................................................ ................25
Chapter 2. Provisions of the theory of fuzzy sets.................................................... 32
§ 2.1. Fuzzy set. Operations on fuzzy sets…..32
§ 2.1.1. Basic operations on fuzzy sets....................................35
§ 2.2. Construction of the membership function...................................................38
§ 2.2.1. Some methods for constructing a membership function......39
§ 2.3. Fuzzy numbers........................................................ ................................44
§ 2.4. Operations with fuzzy numbers (L-R) type....................................................46
§ 2.5. Fuzzy and linguistic variables...................................................47
§ 2.6. Fuzzy relationships......................................................... ........................50
§ 2.7. Fuzzy logic................................................... ................................51
§ 2.8. Fuzzy conclusions........................................................ ...............................53
§ 2.9. Automation of information processing using
fuzzy systems........................................................ ................................59
Chapter 3. Basic intelligent systems....................................................64
§ 3.1. Data and knowledge........................................................ ................................64
§ 3.2. Models of knowledge representation......................................................... .........66
§ 3.3.1. Product rules................................................... ...............69
§ 3.3.2. Frames........................................................ ........................................72
§ 3.3.3. Semantic networks........................................................ ......................74
§ 3.4. Expert systems. Subject areas...................................76
§ 3.5. Purpose and scope of expert systems.................................77
§ 3.6. Methodology for developing expert systems...................................81
§ 3.7. Basic expert systems........................................................ .........86
§ 3.8. Difficulties in developing expert systems and ways to solve them
overcoming................................................... ....................................90
§ 3.9. Purpose, classification of robots.................................................... 94
§ 3.10. Examples of robots and robotic systems............................................97
§ 3.10.1. Home (household) robots.................................................... ....97
§ 3.10.2. Rescue and research robots....................................99
§ 3.10.3. Robots for industry and medicine........................100
§ 3.10.4. Military robots and robotic systems.................................101
§ 3.10.5. The brain as an analog-to-digital device....................................104
§ 3.10.6. Robots - toys......................................................... ....................104
§ 3.11. Problems of technical implementation of robots...................................105
§ 3.12. Adaptive industrial robots...................................................114
§ 3.12.1. Adaptation and training......................................................... .............114
§ 3.12.2. Classification of adaptive control systems
industrial robots........................................................ ...117
§ 3.12.3. Examples of adaptive robot control systems............123
§ 3.12.4. Problems in creating industrial robots...................128
§ 3.13. Neural network and neurocomputer technologies.................................132
§ 3.13.1. General characteristics of the direction...................................132
§ 3.13.2. Neuropackets........................................................ ...........................140
§ 3.14. Neural networks................................................ ........................147
§ 3.14.1. Perceptron and its development.................................................... .....147
3.14.1.1. McCulloch-Pitts mathematical neuron................147
3.14.1.2. Rosenblatt's Perceptron and Hebb's Rule....................................148
3.14.1.3. Delta Rule and Letter Recognition....................................150
3.14.1.4. Adaline, madaline and the generalized delta rule..........152
§ 3.14.2. Multilayer perceptron and inverse algorithm
error propagation................................................................ .....155
§ 3.14.3. Types of activation functions...................................................160
Introduction
The science called “artificial intelligence” is part of the complex computer science, and the technologies created on its basis belong to information technologies. The task of this science is to provide intelligent reasoning and action with the help of computer systems and other artificial devices. As an independent scientific field, artificial intelligence (AI) has existed for just over a quarter of a century. During this time, society's attitude towards specialists engaged in such research has undergone an evolution from skepticism to respect. In advanced countries, work in the field of intelligent systems is supported at all levels of society. There is a strong opinion that it is these studies that will determine the nature of the information society, which is already replacing industrial civilization, which reached its highest point of prosperity in the 20th century. Over the past years of the formation of AI as a special scientific discipline, its conceptual models have been formed, specific methods and techniques that belong only to it have accumulated, and some fundamental paradigms have become established. Artificial intelligence has become a completely respectable science, no less honorable and necessary than physics or biology.
Artificial intelligence is an experimental science. The experimental nature of AI lies in the fact that by creating certain computer representations and models, the researcher compares their behavior with each other and with examples of solving the same problems by a specialist, modifies them based on this comparison, trying to achieve a better match of the results. In order for modification of programs to improve results in a “monotonous” way, one must have reasonable initial ideas and models. They come from psychological studies of consciousness, in particular cognitive psychology.
An important characteristic of AI methods is that it deals only with those competence mechanisms that are verbal in nature (allowing symbolic representation). Not all the mechanisms that a person uses to solve problems are like this.
The book presents the basics of AI, which make it possible to navigate a large number of publications devoted to the problems of artificial intelligence and gain the necessary knowledge in this field of science.
Development of artificial intelligence
History of artificial intelligence started not too long ago. In the second half of the 20th century, the concept was formulated artificial intelligence(artificial intelligence) and several definitions have been proposed. One of the first definitions, which, despite the considerable breadth of interpretation, has not yet lost its relevance, is to present artificial intelligence as: “A way to force computer think like a person."
The relevance of the intellectualization of computing systems is due to the human need to find solutions in such realities modern world, as inaccuracy, ambiguity, uncertainty, vagueness and unreasonableness of information. The need to improve speed and adequacy this process stimulates the creation of computing systems, through interaction with the real world by means of robotics, production equipment, instruments and other hardware, can contribute to its implementation.
Computing systems, the basis of which is based exclusively on classical logic - that is, algorithms for solving known problems, encounter problems when encountering uncertain situations. In contrast, living beings, although inferior in speed, are capable of making successful decisions in such situations.
Artificial Intelligence Example
An example is the collapse stock market 1987, when computer programs sold shares worth hundreds of millions of dollars in order to make a profit of several hundred dollars, which actually created the preconditions for the collapse. The situation was corrected after the transition full control behind stock trading in protoplasmic intelligent systems, that is, to people.
Defining the concept of intelligence as a scientific category, it should be understood as the suitability of a system for learning. Thus, one of the most specific, in our opinion, definitions of artificial intelligence is interpreted as the ability of automated systems to acquire, adapt, modify and expand knowledge in order to find solutions to problems whose formalization is difficult.
In this definition, the term “knowledge” has a qualitative difference from the concept of information. This difference is well reflected in the representation of these concepts in the form information pyramid in Fig. 1.
Figure 1 - Information pyramid
It is based on data next level occupied by information, the level of knowledge completes the pyramid. As you move up the information pyramid, the volume of data turns into the value of information and then into the value of knowledge. That is, information arises at the moment of interaction between subjective data and objective methods of processing them. Knowledge is formed on the basis of the formation of distributed relationships between heterogeneous information, while creating a formal system - a way of reflecting them in precise concepts or statements.
It is the support of such a system - a knowledge system, in such an up-to-date state, which allows one to build action programs to find solutions to the tasks assigned to them, taking into account the specific situations that arise at a certain point in time in the environment, that is the task of artificial intelligence. Thus, artificial intelligence can also be imagined as a universal over-algorithm capable of creating algorithms for solving new problems.
The textbook introduces readers to the history of artificial intelligence, knowledge representation models, expert systems and neural networks. The main directions and methods used in the analysis, development and implementation of intelligent systems are described. Models of knowledge representation and methods of working with them, methods of developing and creating expert systems are considered. The book will help the reader master the skills of logical design of domain databases and programming in the ProLog language.
For students and teachers of pedagogical universities, teachers of secondary schools, gymnasiums, lyceums.
The concept of artificial intelligence.
An artificial intelligence (AI) system is a software system that simulates the human thinking process on a computer. To create such a system, it is necessary to study the very thinking process of a person solving certain problems or making decisions in a specific area, highlight the main steps of this process and develop software that reproduces them on a computer. Therefore, AI methods take a simple structured approach to developing complex software decision-making systems.
Artificial intelligence is a branch of computer science whose goal is to develop hardware and software that allows a non-programmer user to pose and solve their traditionally considered intellectual problems, communicating with a computer in a limited subset of natural language.
TABLE OF CONTENTS
Chapter 1. Artificial Intelligence
1.1. Introduction to Artificial Intelligence Systems
1.1.1. The concept of artificial intelligence
1.1.2. Artificial intelligence in Russia
1.1.3. Functional structure of an artificial intelligence system
1.2. Directions for the development of artificial intelligence
1.3. Data and knowledge. Representation of knowledge in intelligent systems
1.3.1. Data and knowledge. Basic definitions
1.3.2. Knowledge representation models
1.4. Expert systems
1.4.1. Expert system structure
1.4.2. Development and use of expert systems
1.4.3. Classification of expert systems
1.4.4. Representation of knowledge in expert systems
1.4.5. Tools for building expert systems
1.4.6. Expert system development technology
Test questions and assignments for Chapter 1
References for Chapter 1
Chapter 2. Logic programming
2.1. Programming methodologies
2.1.1. Imperative programming methodology
2.1.2. Object-oriented programming methodology
2.1.3. Functional programming methodology
2.1.4. Logic programming methodology
2.1.5. Constraint Programming Methodology
2.1.6. Neural network programming methodology
2.2. A Brief Introduction to Predicate Calculus and Theorem Proving
2.3. Inference Process in Prolog
2.4. Program structure in Prolog language
2.4.1. Using Composite Objects
2.4.2. Using alternative domains
2.5. Organizing repetition in Prolog
2.5.1. Rollback method after failure
2.5.2. Cut and rollback method
2.5.3. Simple recursion
2.5.4. Generalized recursion rule (GRR) method
2.6. Lists in Prolog
2.6.1. Operations on lists
2.7. Strings in Prolog
2.7.1. String Operations
2.8. Files in Prolog
2.8.1. Prolog predicates for working with files
2.8.2. File domain description
2.8.3. Write to file
2.8.4. Reading from a file
2.8.5. Modifying an existing file
2.8.6. Appending to the end of an existing file
2.9. Creating dynamic databases in Prolog
2.9.1. Databases in Prolog
2.9.2. Dynamic Database Predicates in Prolog
2.10. Creation of expert systems
2.10.1. Expert system structure
2.10.2. Knowledge representation
2.10.3. Withdrawal Methods
2.10.4. User Interface System
2.10.5. Rule-based expert system
Test questions and assignments for Chapter 2
References for Chapter 2
Chapter 3. Neural networks
3.1. Introduction to Neural Networks
3.2. Artificial neuron model
3.3. Application of neural networks
3.4. Neural network training
Test questions and assignments for Chapter 3
References for Chapter 3.
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