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Comments on the current BSE epidemic taking a biocybernetical view. Produces a possible scenario which could explain how protein might replicate, albeit indirectly, using DNA as its agent.

In this paper, a biocybernetic method to learn hand grasping posture definition with few knowledge about the task is proposed. The developed model is composed of two stages. The first is dedicated to the fingers inverse kinematics learning in order to locally define a single finger posture given its desired fingertip position. This motor function is fulfilled by a modular neural network architecture that tackles the discontinuity problem of inverse kinematics function (called Fingers Configuration Neural Network (FCNN)). Following the concept of direct associative learning, a second neural model is used to search the space of hand configuration and optimize it according to an evaluative function based on the results of the FCNN. Simulation results show good learning of grasping posture determination of various object types, with different numbers of fingers involved and different contact configurations.

This paper develops a hypothesis concerning the structure and interaction principles of main psychological programs in form of semantic networks. Models for the brain information mechanisms are proposed, including planning, decision making, complex concept forming and situation evaluation. Some experimental studies are described.

A kinetic model for the biphasic modulation of phagocytosis, fulfilling the requirements of biochemistry of ligand‐binding reactions, was constructed on the basis of a biocybernetic notion of the feedback loop‐containing (autocalytic) process. Using boundary conditions for parameters of possible kinetic models, a single model was selected, in which the biphasic modulatory effect exerted on a phagocytic activity of human neutrophils by the peptide preparation Immax A¹ was described as a result of mutual counteraction of two antagonistic compounds (stimulator and inhibitor of phagocytosis) competing for bacterial chemotactic peptide receptors on neutrophils. This model, in which the integrated luminescence‐based normalised measure of inhibition of phagocytosis stands for the reaction rate, was found to have a form of a 2:3 rational function of the peptide preparation concentration. A corresponding stoichiometric scheme, describing the binding both of the inhibitor and of the stimulator to neutrophils, was constructed on the assumption that inhibition was not total when connected with the generation of three‐component complexes, stimulator‐neutrophil‐inhibitor.

In this paper, the authors aim to propose a method for learning robotic assembly sequences, where precedence constraints and object relative size and location constraints can be learned by demonstration and autonomous robot exploration.

To successfully plan the operations involved in assembly tasks, the planner needs to know the constraints of the desired task. In this paper, the authors propose a methodology for learning such constraints by demonstration and autonomous exploration. The learning of precedence constraints and object relative size and location constraints, which are needed to construct a planner for automated assembly, were investigated. In the developed system, the learning of symbolic constraints is integrated with low-level control algorithms, which is essential to enable active robot learning.

The authors demonstrated that the proposed reasoning algorithms can be used to learn previously unknown assembly constraints that are needed to implement a planner for automated assembly. Cranfield benchmark, which is a standardized benchmark for testing algorithms for robot assembly, was used to evaluate the proposed approaches. The authors evaluated the learning performance both in simulation and on a real robot.

The authors' approach reduces the amount of programming that is needed to set up new assembly cells and consequently the overall set up time when new products are introduced into the workcell.

In this paper, the authors propose a new approach for learning assembly constraints based on programming by demonstration and active robot exploration to reduce the computational complexity of the underlying search problems. The authors developed algorithms for success/failure detection of assembly operations based on the comparison of expected signals (forces and torques, positions and orientations of the assembly parts) with the actual signals sensed by a robot. In this manner, all precedence and object size and location constraints can be learned, thereby providing the necessary input for the optimal planning of the entire assembly process.

This paper details advances in biocybernetics and gives reports and surveys of selected research and development in systems and cybernetics. They include supercomputers, biometric technology, formal methods, applications of nanotechnology, innovations, and cybercriminals.

There exist a number of urgent problems in psychotherapy, social psychology and sociology which can be solved by means of special methods that would enable one to predict the probable behaviour of man in certain situations. This paper discusses one of these methods and gives a concise description of two heuristic personality models in the form of computer programs.

A model is constructed describing the interaction of systems of carbohydrate and fatty‐lipoid metabolism. The model equations can be presented in the form of a system of linear differential equations which combines 36 velocities of different metabolic and hormonal variances in different tissues into a system of combined feedback loops. A simplified variant is given of the model of a system of regulating sugar in blood. The mathematical model being available, it is possible to choose proper dosage. Mathematical models are used for practical medical applications.

Cybernetics has an important application in Biology, particularly in explaining how the brain might work. The brain can best be considered as two organs and not one, grey matter and white matter, with different theories to explain the function of each. The current “living transistor theory” seems inadequate to explain the function of neurons which individually appear to feel pleasure and pain and might, therefore, obey some principle of selection of appropriate behaviour similar to that of evolution by natural selection. Finally, any consideration of how the brain treats knowledge demands some clear idea of what knowledge is and how it may be coded in chemical terms.