9554976
Evolutionary Divide and Conquer (I): A Novel Genetic Approach to the TSP
Experiments with genetic algorithms using permutation operators applied to the traveling salesman problem (TSP) tend to suggest that these algorithms fail in two respects when applied to very large problems: they scale rather poorly as the number of cities n increases, and the solution quality degrades rapidly. We propose an alternative approach for genetic algorithms applied to hard combinatoric search which we call Evolutionary Divide and Conquer (EDAC). This method has potential for any search problem in which knowledge of good solutions for subproblems can be exploited to improve the solution of the problem itself. The idea is to use the genetic algorithm to explore the space of problem subdivisions rather than the space of solutions themselves. We give some preliminary results of this method applied to the geometric TSP.
11348191
A note on the Gamma test
This note describes a simple technique, the Gamma (or Near Neighbour) test, which in many cases can be used to considerably simplify the design process of constructing a smooth data model such as a neural network. The Gamma test is a data analysis routine, that (in an optimal implementation) runs in time O(MlogM)as M→∞,where Mis the number of sample data points, and which aims to estimate the best Mean Squared Error (MSError) that can be achieved by any continuous or smooth (bounded first partial derivatives) data model constructed using the data.
9858437
Heuristic Confidence Intervals for the Gamma Test
The Gamma test is a fast non-parametric algorithm for estimating the noise variance in an input/output dataset modulo the best smooth model. In this short note we communicate a heuristic method for computing confidence intervals for the noise estimate returned by a Gamma test analysis.
14418558
Adaptive neuro-genetic control of chaos applied to the attitude control problem
Conventional adaptive control techniques have, for the most part, been based on methods for linear or weakly non-linear systems. More recently, neural network and genetic algorithm controllers have started to be applied to complex, non-linear dynamic systems. The control of chaotic dynamic systems poses a series of especially challenging problems. In this paper, an adaptive control architecture using neural networks and genetic algorithms is applied to a complex, highly nonlinear, chaotic dynamic system: the adaptive attitude control problem (for a satellite), in the presence of large, external forces (which left to themselves led the system into a chaotic motion). In contrast to the OGY method, which uses small control adjustments to stabilize a chaotic system in an otherwise unstable but natural periodic orbit of the system, the neuro-genetic controller may use large control adjustments and proves capable of effectively attaining any specified system state, with no a prioriknowledge of the dynamics, even in the presence of significant noise.
14169547
Non-linear modelling and chaotic neural networks
Proposes a simple methodology to construct an iterative neural network which mimics a given chaotic time series. The methodology uses the Gamma test to identify a suitable (possibly irregular) embedding of the chaotic time series from which a one step predictive model may be constructed. This model is then iterated to produce a close approximation to the original chaotic dynamics. Having constructed such networks we show how the chaotic dynamics may be stabilised using time-delayed feedback, which is a plausible method for stabilisation in biological neural systems. Using delayed feedback control, which is activated in the presence of a stimulus, such networks can behave as an associative memory, in which the act of recognition corresponds to stabilisation onto an unstable periodic orbit. We briefly illustrate how two identical dynamically independent copies of such a chaotic iterative network may be synchronised using the delayed feedback method. Although less biologically plausible, these techniques may have interesting applications in secure communications.
3067787
Neuro-genetic adaptive attitude control
It has previously been demonstrated that for smooth dynamic systems, using relatively few sample points from a single trajectory, a neural network can be trained to perform very accurate short-term prediction over a large part of the phase space. In this paper, we exploit the capability of a Locally Predictive Network (LPN) to derive an adaptive control architecture for a satellite equipped with controllable, bidirectional thrusters on each of the three principal axes. It is assumed that a hardware implementation of the neural network is available. The inputs for the network are a small history of system states up to the present time and a set of current control inputs, the outputs are the next system state. Once the LPN has been trained successfully, at each time step a genetic algorithm searches the space of hypothetical control inputs. Given a set of control signals, the LPN is used to predict the state of the system at the next sample point. This enables the ‘fitness’ of each set of hypothetical control torques to be evaluated very rapidly. In effect, the genetic algorithm determines a satisfactory solution to the inverse kinematic problem in time to apply the solution (set of control torques) at the next control point. With the exception of the neuromodelling (which is repeated only when the system dynamics change), the whole process is then repeated. The results presented indicate that such an architecture is easily able to master the attitude control problem for arbitrary slew angles, with arbitrary a priori unknowndynamics and noise in the sensor system.
47276
A multi-processing software infrastructure for robotic systems
Robotic systems and their software design are based on the same principles found in other computer applications. However, while many computer systems to date are deeply rooted in concepts such as multi-processing, multi-threads, modularity, etc., robotic systems are still limited by mono-processing, centralized designs. The paper addresses these limitations by proposing a new philosophy for robotic system software design - software infrastructure $that allows for the design of an efficient, modular, fault-tolerant, and distributed software architecture. Two examples of this infrastructure applied to mobile robot navigation are also discussed.
20473212
Towards a formal theory of defeasible deontic conditionals
The paper outlines the approach to the analysis of deontic conditionals taken in earlier work of Jones and Pörn, comparing it briefly with two main trends within dyadic deontic logic, and discussing problems associated with the augmentation principle and the factual detachment principle. A modification of the Jones and Pörn system is then presented, using a classical but not normal (in the sense of Chellas) deontic modality, to provide the basis for an alternative analysis of deontic conditionals. This new analysis validates neither the factual detachment nor the augmentation principles. However, influenced by the approach of Delgrande to default reasoning, it is shown how a restricted form of factual detachment might be accommodated within the revised system.
6839309
Five 1951 BBC broadcasts on automatic calculating machines
In May and June 1951, five leading figures of British computing - Douglas Hartree, Max Newman, Alan Turing, Frederic ("Freddie") Williams, and Maurice Wilkes - spoke about their work on BBC radio. We examine surviving texts of their broadcasts, and the speakers' principal points are summarized through quotations and commentary. The broadcasts are placed in the context of contemporary developments in computing and the particular BBC service on which they were broadcast.
207571365
An APL Approach to Presentation Graphics
Producing data in pictorial form is a type of computer graphics application known as presentation graphics. One approach that has been used for this type of graphics is a graphic support package using APL as the command language. Here discussed is the evolution of this approach up to its currently available forms.
206840293
Formal Specification of Security Requirements using the Theory of Normative Positions
We use a number of the examples presented in [Ting 1990] to illustrate how the formal theory of normative positions may serve as a tool for clarifying, and making precise, the specification of security requirements, particularly in regard to access control. We describe the basic features of the theory of normative positions (which has its roots in the analytical theory of law), and of the modal logics (deontic and action logics) involved in its formulation. We then indicate three levels of software we have under development, which aim to turn the analytical procedures into a practical tool. Our concluding remarks relate our discussion of Ting's examples to some particular issues in the formal specification of computer systems.