Action Of System Research Articles

Constants underlying frequency changes in biological rhythmic movements.

When an animal increases or decreases the frequency of its limb motions, how should the transformation in timing be characterized? It has been hypothesized that the transformation is adiabatic, even though the biological conditions are nonconservative and non-rate-limited (Kugler and Turvey 1987). An adiabatic transformation requires that the rhythmic system's action (energy/frequency) and entropy production remain time-invariant throughout the transformation. The non-conservative adiabatic hypothesis was evaluated through an experiment on human rhythmic hand movements. On each trial, a subject began at a prescribed frequency and then, over a 30 s interval, increased (or decreased) the frequency continuously at will. For each subject, on each increasing and decreasing trial, cycle kinetic energy was a linear function of cycle frequency with a negative energy intercept. By the adiabatic hypothesis, the slope of the function defines the constant action and the intercept defines the constant dissipation - changes in cycle frequency incur no changes in energy dissipated per cycle. Slopes and intercepts were correlated suggesting a common basis for the two constants, and the variety of cycle amplitude-cycle duration relations were in agreement with the nonmonotonic, nonlinear space-time function predicted by the hypothesis. The possibilities of addressing aspects of the data through (a) muscle modeled as a continuum of Kelvin bodies with a continuous relaxation spectrum, and (b) various classes of autonomous differential equations, were discussed. Most importantly, the discussion focused on the puzzling independence of energy cost and speed exhibited by locomoting animals differing in morphology, physiology, size, and taxa. It was suggested that the independence may reflect a very general principle - adiabatic transformability of biological movement systems.

Mobile robot control by neural networks using self-supervised learning

A reinforcement learning algorithm based on supervised learning is described. It uses associative search to discover and learn actions that make the system perform a desired task. One problem with associative search is that the system's actions are often inconsistent. In the searching process, the system's actions are always decided stochastically, so the system cannot perform learned actions more than once, even if they have been determined to be suitable actions for the desired task. To solve this problem, a neural network that can predict an evaluation of an action and control the influence of the stochastic element is used. Results from computer simulations using the algorithms to control a mobile robot are described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Explanations in knowledge systems: design for explainable expert systems

The explainable expert systems framework (EES), in which the focus is on capturing those design aspects that are important for producing good explanations, including justifications of the system's actions, explications of general problem-solving strategies, and descriptions of the system's terminology, is discussed. EES was developed as part of the Strategic Computing Initiative of the US Dept. of Defense's Defense Advanced Research Projects Agency (DARPA). both the general principles from which the system was derived and how the system was derived from those principles can be represented in EES. The Program Enhancement Advisor, which is the main prototype on which the explanation work has been developed and tested, is presented. PEA is an advice system that helps users improve their Common Lisp programs by recommending transformations that enhance the user's code. How EES produces better explanations is shown.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The elementary catalytic system model as a building unit of large enzymatic systems: General model

A model of an elementary catalytic system (ECS) is suggested, derived from the notions of the general theory of systems, which is capable of performing a single elementary catalytic act (ECA). For large chemical systems involving high numbers of ECAs occurring on various ECSs, the system's action becomes increasingly effective when component ECSs are coupled into complex structures. Models are suggested to describe these couples and several fundamental theorems are proved concerning the possible structures. Complex catalytic systems are shown to contain finite numbers of ECSs.

The loading-frequency relation of linear conservative systems via a direct energetic (action) method

This paper presents alternative proofs of the well-known strict negative monotonicity and convexity properties of the fundamental frequency-load pair curve of the pure eigenproblem of free vibrations/buckling of linear conservative systems subject to a single loading parameter, by studying the first and second order variations of the system's Hamiltonian action (over the fundamental or highest eigenperiod), as one moves along the fundamental eigenpair curve ( FEC ). Specifically, by setting the action's first and second frequency derivatives equal to zero it is shown that, all along the FEC , both the first and second derivatives of the load with respect to the frequency keep a strict negative sign; these action derivatives are special cases of the general first and second variations of a variable endpoints variational problem. This energetic method opens the way for the development of an asymptotic scheme (by taking third, fourth and higher action derivatives) for the approximate study of the FEC shape in the neighborhood of a given point (reminiscent perhaps of Koiter's asymptotic elastostatic post-buckling theory). At this point, the method does not seem to be restricted by linearity neither in the equations of motion nor in their eigenload(s) dependence.

A DEVELOPMENTAL APPROACH TO PURPOSEFUL SYSTEMS AND INTELLIGENCE

In order to survive, the actions of a purposeful system must be governed in such a way that its more prepotent needs are satisfied. A purposeful system's actions are largely governed by its representation of its world, and hence if it has an inappropriate representation it will not survive. Our premise is that intelligence is a property only reasonably attributable to purposeful systems which compute a representation of their world. Using this premise, we consider cognition to be the continual process of computing/recomputing representations and argue that it implies forms of computation, described in this paper, which make distinctions.