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If information science is to be considered a “science” in the true sense of the word, there is a clear need for the development of a set of general concepts and analytical expressions regarding the flow of information in any situation for which information serves as a resource. This paper discusses quantitative aspects of a theory leading to the development of such a pragmatic information theory . At the heart of a theory of pragmatic information is the measure of information contained in a set of data. At the pragmatic level, information has value to the extent that it is useful as a resource for purposeful activity. The primary “purposeful activity” in life is decision-making. Hence, information and decision-making are inextricably tied together; in fact, information is data of value in decision-making . In this paper, we discuss quantitatively a very general decision model which provides a formal and comprehensive representation of uncertainty in decision-making. Other decision models presented in the literature assume away a considerable and important portion of the uncertainty that exists in decision-making. This model is then used as a framework for examining the role of information in decision-making in a way that is also formal and comprehensive. Hence, the decision model suggested facilitates a meaningful analysis of information; this analysis culminates in the development of a measure of the value of the pragmatic information contained in a set of data.

This paper describes a fuzzy logic anti-slip system for Heavy Duty Off Road Vehicles. The anti-slippage system is based on distributed detection and local/global fuzzy control of slippage within an interconnected system of mechatronic wheel motors. Within this paper, the system layout, implementation, sensor data preprocessing, slip detection, four different local/global and test results under real working conditions are described. The system developed may serve as an example for successfully applied fuzzy control for a competitive (soon) commercially available product.

The present paper is closely related to an earlier publication wherein the present authors proposed a mathematical formulation for continuous nonlinear minimal-variance filters that led to stochastic differential equations satisfying known existence and uniqueness conditions for such equations. The necessity for the proposed reformulation of the problem arose from the use of the white-noise model to represent the physical system. The adoption of such a model allows certain formal simplicities in the derivation of filter equations, but these simplicities must be paid for by new complexities introduced by the requirement for mathematical rigor, as discussed in the previous paper. It is now shown that the modified formulation, while necessary for assured mathematical validity, can be dispensed with in the context of a computational mechanization.

This paper describes procedures for the design of experiments to determine whether or not a given finite-state machine is operating correctly. The machines in question are assumed to be strongly connected and in a reduced form. The procedures presented are valid for any type of faults that do not increase the number of states of the original machine. More efficient procedures are presented in the last section, when certain restrictions are imposed on the class of possible faults.

This paper presents the innovations approach to space-time or distributed parameter filtering and smoothing. The innovations process is defined for systems with space-time coordinates and properties of this process are derived. Development of a special operator notation allows a very simple presentation of the algorithms for estimation. The filtering results are in agreement with those obtained by other derivations whereas it is believed that the fixed-interval and fixed-point smoothing algorithms for space-time systems are new.

By extracting the basic properties common to the formal grammars appeared in existing literatures, we develop a general formulation of formal grammars. We define a pseudo grammar and derive from it the well-known probabilistic, fuzzy grammars and so on. Moreover, several interesting grammars such as ⊔∗ grammars, ⊔ ⊓ grammars, ⊔ ⊓ grammars, composite B -fuzzy grammars, and mixed fuzzy grammars, which have never appeared in any other papers before, are derived.

In the past, automatic procedures for the design of pattern classifiers have usually used a machine of fixed size. These procedures require that prior to learning we know, or can guess, the complexity of the decision surface used by the teacher. Techniques have been discovered for changing the complexity of a pattern classifier by adding or removing parts of it. These methods, respectively called “growing” and “pruning” use well established learning rules applied alternately with the addition or removal of comput,ing equipment. Growing is thus a process by which we can increase the complexity of a classifier until it fits the problem. Pruning removes computing equipment from a classifier whose performance is satisfactory, but inefficient. The paper describes our experimental evaluation of these procedures. These studies have shown that these techniques are capable of designing a classifier which closely models a complex teacher with a minimum of storage.

In this paper, properties of normalized stochastic languages are discussed and alternative procedures for constructing the Chomsky and Greibach normal forms for normalized stochastic context-free grammar (nscfg) are presented. A normalized stochastic context-free language (nscf l) is defined in terms of a nscfg. Furthermore, stochastic languages accepted by stochastic pushdown automata (spda) are defined, and relationships between stochastic context-free languages and spda are studied. The class of languages accepted by a spda with a 0 cutpoint is precisely the class of scf l.

This paper uses optimization theory and continuous invariant imbedding in order to derive computationally efficient sequential algorithms for the maximum likelihood estimation of bias errors in recursive filtering with noise corrupted input observations and correlated plant and measurement noise. Error analysis algorithms are derived for adaptive and nonadaptive systems with bias and modeling errors. Examples demonstrate the efficacy of the adaptive estimation algorithms and the error analysis algorithms for estimation with bias uncertainty.

The purpose of this paper is to investigate various properties of some learning automata operating in random environments. Based on the strategies of ''many-armed bandit problems,'' two sequential learning models, A"r","k and B"r","k","b, having deterministic and stochastic transition rules, respectively, are formulated. Automata using these strategies have shown the desired learning behavior, which is similar to the performance of linear-strategy automata and certain type of stochastic automata. Furthermore, the learning behavior of fuzzy automata in random environments is also considered. Comparison among various models has been made. Computer-simulated examples for the application of proposed models include games against nature and games between automata. The results from simulation have been considered quite satisfactory.

This paper presents the problem of unsupervised learning structure and parameter adaptive pattern recognition. Different near optimal solutions that alleviate the infinite or exploding memory requirements of the optimal solution are suggested. The learning capabilities of these methods are compared with that of the optimal supervised scheme by presenting an example.

This paper deals with a nonlinear learning algorithm characterized by a threshold effect. The algorithm is derived in relation to a learning process in pattern recognition where steps must be taken to minimize the effect of spurious samples carrying unreliable information. With the assumption of a multivariate normal density for each pattern population, the nonlinear algorithm reduces to discarding the spurious samples with a threshold element and in the meantime performing an ordinary linear algorithm for the remaining samples. Optimal properties of this algorithm are studied and a criterion for selecting a proper threshold is discussed. A computer-simulated experiment in character recognition is presented to illustrate a possible application for the nonlinear algorithm. Results of this experiment have shown significant improvement over an ordinary linear algorithm in handling character samples with various imperfections due to the poor quality in writing or printing encountered in practice.

This paper describes the proceedings of the workshop on Biomedical Statistics and Computation of the Fourth Annual Symposium on the Interface. This was a two day symposium held at the University of California at Irvine on September 17 & 18, 1970. It was sponsored jointly by the ACM, ASA, and the California Mathematics Council. Dr. Tarter chaired this workshop. The thirty participants in this workshop represented all of the disciplines which combine to become Biocomputer Statistics. There were physicians both interested in and active in computer applications to medicine, directors of biomathematics and biostatistics programs, computer specialists, and mathematical and applied statisticians, as well as medical students. It should be noted that the three other workshops at the Interface Symposium were more concerned with computer hardware and software and thus attracted most of the pure computer specialists. This tended to weight this workshop with a large proportion of statisticians.

This paper proposes a family of new methods for the storage and reconstruction of binary patterns by means of block-projections. More flexibility and less ambiguity is thus achieved. A complete analysis is presented in terms of (i) storage requirements, (ii) computation, and (iii) unambiguity ratio. Furthermore, applications to general patterns are described.

TUTOR is a high speed Turing machine simulator, written in IBM System 360 Assembler Language. Turing machines are represented by flow diagrams using H. Hernes table of machines as the basic elements. Composite machines so represented may themselves be used as elements of other machines, thus incorporating the concept of subroutines. In this paper the relationship of a flow diagram to the basic concept of a Turing machine is developed, the functions of the basic machines are described, and a complete programmer's guide is included.

In this paper we describe an approach to the artificial recognition of events of a nonsymbolic nature, such as the bidimensional perspective views of scenes of our everyday world. Scenes are presented as colored pictures and the objective of the cognitive task is the labeling of the interpreted scene objects. The method is based on three major components: 1. (i) a preprocessed version of the scene (stimulus), 2. (ii) a semantic map and 3. (iii) an algorithm which performs interpretation of the stimulus under the guidance of the semantic map. The algorithm is sequential and proceeds from general to specific, thereby achieving efficient tree-pruning (contextual elimination). Stimulus interpretation is based on attribute-value matching, but classification relies strongly on the accumulated context. Backtracking provisions are available in the sequential cognitive algorithm for correction of earlier wrong hypotheses. Experiments are presented and described. The major weakness of the approach is the present lack of a satisfactory theory of inference. Flexibility, generalizability, and efficiency appear to be valid merits.