Advances in Information Systems Science : Volume 1

Engineering has long been thought of by the public as a profession tra­ ditionally categorized into such branches as electrical, mechanical, chemical, industrial, civil, etc. This classification has served its purpose for the past half century; but the last decade has witnessed a tremendous change. A continuous transition from the practical to the theoretical has made technology overlap with science, and the enlargement of scope and broad­ ened diversification have smeared the boundaries between traditional engi­ neering and scientific fields. Engineering is rapidly becoming a diversified, multidisciplinary field of scientific endeavor. This has prompted us to regard modern engineering as a science, which has as its ingredients materials, energy, and information. In our complex and technologically-oriented society organizations are flooded with an enormous amount of management information. We are now faced with problems concerning the efficient use of communicated knowledge. The steady growth in the magnitude and complexity of informa­ tion systems necessitates the development of new theories and techniques for solving these information problems. We demand instant access to pre­ viously recorded information for decision making, and we require new meth­ ods for analysis, recognition, processing, and display. As a consequence, information science has evolved out of necessity. Concerned with the theoretical basis of the organization, control, stor­ age, retrieval, processing, and communication of information both by natural and artificial systems, information science is multidisciplinary in character. It covers a vast area of subject matter in the physical and biological sciences.

1 Theory of Algorithms and Discrete Processors.- 1. Introduction.- 2. Discrete Processors.- 3. Examples of Discrete Processors.- 3.1. Turing Machines.- 3.2. Markov’s Normal Algorithms.- 3.3. Kaluzhnin’s Graph Schemata and Logical Schemata of Algorithms.- 3.4. Programs in Algorithmic Languages.- 4. Computers and Discrete Processors.- 5. Systems of Algorithmic Algebras.- 6. Application of Algorithmic Algebras to Transformations of Microprograms.- 7. Equivalence of Discrete Processors.- 8. Equivalence of Automata with Terminal States Relative to an Automaton without Cycles.- 9. Specific Cases of Solutions to the Equivalence Problem.- 10. Conclusions.- References.- 2 Programming Languages.- 1. Introduction.- 2. The Basic Linguistic Nature of Programming Languages.- 2.1. Language and Communication.- 2.2. The Necessity of Rigor.- 2.3. Programs and Jobs.- 3. Programming Languages and Semiotics.- 3.1. The Three Branches of Semiotics.- 3.2. Programming Languages and Programming Systems.- 4. The Formal Definition of Programming Lan guages.- 4.1. Syntax.- 4.2. The Role of Declarations. Languages and Linguistic Systems.- 4.3. Semantics and Pragmatics.- 5. The Definition of Programmable Automata and their Languages.- 6. Parallel Concurrent Processes.- 7. Machine Languages.- 7.1. Direct Machine Languages.- 7.2. Symbolic Machine Languages.- 8. Special and General-Purpose Algorithmic Languages.- 8.1. Numerical Algorithmic Languages.- 8.2. Commercial and File Processing Languages.- 8.3. Symbol Manipulation Languages.- 8.4. General-Purpose Algorithmic Languages..- 9. Special Problem-Oriented Languages.- 9.1. Problem-Defining Languages.- 9.2. Programming Languages for Numerically Controlled Machines.- 9.3. Picture Manipulation Languages.- 10. Simulation Languages.- 10.1. Simulation Languages and Dynamical Systems.- 10.2. Discrete Simulation Languages.- 10.3. Continuous Simulation Languages.- 11. Conversational Languages.- 12. Conclusion.- References.- 3 Formula Manipulation—The User’s Point of View.- 1. Introduction.- 1.1. The Nature of Formula Manipulation.- 2. Different Types of Formula Manipulation Systems.- 2.1. Polynomials and Rational Functions ….- 2.2. Analytical Methods.- 2.3. Definitional Facilities.- 2.4. Interactive Systems and Methods.- 3. Toward a Mathematical Utility.- 4. The Formula Manipulation Language Symbal.- 5. The Syntax of Symbal.- 5.1. The Basic Symbols.- 5.2. The Basic Syntactic Elements.- 5.3. Expressions.- 5.4.Vectors.- 5.5. Statements and the Block Structure.- 5.6. Quotations.- 6. The Basic Symbols and Syntactic Entities.- 6.1. Variables, Types, and Values.- 6.2. The Structure of Values.- 7. Expressions.- 7.1. Differentiation and Substitution.- 7.2. The Evaluation of Expressions.- 7.3. The For Clause.- 7.4. The Operators for Sums and Products...- 7.5. The Power of Expressions.- 8. The Remaining Parts of the Language.- 8.1. Vectors.- 8.2. Procedures.- 8.3. Statements and the Block Structure.- 9. Standard Variables.- 9.1. The Modes of Symbal.- 9.2. Control of Simplification.- 9.3. Control of Output.- 10. Techniques and Applications.- 10.1. Numerical Problems.- 10.2. Polynomials and Power Series.- 10.3. Differential Equations.- 10.4. Linear Algebra.- 11. Summary.- References.- 4 Engineering Principles of Pattern Recognition.- 1. Introduction.- 2. Basic Problems in Pattern Recognition.- 3. Feature Selection and Preprocessing.- 3.1. Probability Density Functions.- 3.2. Feature Selection Through Entropy Minimization.- 3.3. Feature Extraction Through Functional Approximation.- 4. Pattern Classification by Distance Functions.- 4.1. Categories Representable by Standard Patterns.- 4.2. Categories Not Representable by Standard Patterns.- 4.3. Realization of Linear Decision Functions.- 4.4. General Decision Functions.- 4.5. Training Algorithms.- 5. Pattern Classification by Potential Functions...- 5.1. Generation of Decision Functions.- 5.2. Geometrical Interpretation and Weight Adjustment.- 5.3. Convergence of Training Algorithms.- 5.4. Realization of Potential-Function Classifier.- 5.5. Probabilistic Pattern Classification Problem.- 6. Pattern Classification by Likelihood Functions.- 6.1. Probabilistic Decision Functions.- 6.2. Normal Patterns.- 6.3. Bayesian Learning of Mean Vectors.- 6.4. Nearest-Neighbor Estimation.- 7. Pattern Classification by Entropy Functions...- 8. Conclusions.- References.- 5 Learning Control Systems.- 1. Introduction.- 2. Trainable Controllers.- 2.1. Least-Mean-Square-Error Training Procedure.- 2.2. Error-Correction Training Procedure.- 3. Reinforcement Learning Control Systems.- 4. Bayesian Learning in Control Systems.- 5. Learning Control Systems Using Stochastic Approximation.- 6. The Method of Potential Functions and its Application to Learning Control.- 6.1. The Estimation of a Function with Noise-Free Measurements.- 6.2. The Estimation of a Function with Noisy Measurements.- 7. Stochastic Automata as Models of Learning Controllers.- 8. Conclusions.- Appendix. Stochastic Approximation—A Brief Survey.- References.- Author Index.