Test incorporations are program transformations that improve the performance of generate-and-test procedures by moving information out of the \test" and into the \generator." The test information is said to be \incorporated" into the generator so that items produced by the generator are guaranteed to satisfy the incorporated test. This article proposes and investigates the hypothesis that a gen...

Hierarchical latent class (HLC) models are tree-structured Bayesian networks where leaf nodes are observed while internal nodes are not. This paper is concerned with the problem of learning HLC models from data. We apply the idea of structural EM to a hill-climbing algorithm for this task described in an accompanying paper (Zhang et al. 2003) and show empirically that the improved algorithm can...

A simple recurrent neural network is trained on a one-step look ahead prediction task for symbol sequences of the context-sensitive a n b n c n language. Using an evolutionary hill climbing strategy for incremental learning the network learns to predict sequences of strings up to depth n = 12. Experiments and the algorithms used are described. The activation of the hidden units of the trained n...

This paper introduces the Extremum Consistency (EC) algorithm for avoiding local maxima and minima in a specialised domain. The most notable difference between this approach and others in the literature is that it places a greater importance on the width or consistency of an extremum than on its height or depth (amplitude). Short-term, high amplitude extrema can be encountered in many typical s...

In this paper we describe an optimal reconfiguration planning algorithm that morphs a grounded truss structure of known geometry into a new geometry. The plan consists of a sequence of paths to move truss elements to their new locations that generate the new truss geometry. The trusses are grounded and remain connected at all time. Intuitively, the algorithm grows gradually the new truss struct...

We investigate the use of oblivious, read-once decision graphs as structures for representing concepts over discrete domains, and present a bottom-up, hill-climbing algorithm for inferring these structures from labelled instances. The algorithm is robust with respect to irrelevant attributes, and experimental results show that it performs well on problems considered di cult for symbolic inducti...

Scarcity of Energy resources, increasing power generation cost and ever-growing demand of electric energy necessitates optimal economic dispatch in today‘s power systems.In this paper presents a computational approach to minimize the total fuel cost in thermal power station using Artificial Immune System (AIS) algorithm. The AIS algorithm is a machine learning approach and a powerful stochastic...

We present an algorithm for the multi-robot simultaneous localization and mapping (SLAM) problem. Our algorithm enables teams of robots to build joint maps, even if their relative starting locations are unknown and landmarks are ambiguous—which is presently an open problem in robotics. It achieves this capability through a sparse information filter technique, which represents maps and robot pos...

We present an algorithm for the multi-robot simultaneous localization and mapping (SLAM) problem. Our algorithm enables teams of robots to build joint maps, even if their relative starting locations are unknown and landmarks are ambiguous— which is presently an open problem in robotics. It achieves this capability through a sparse information filter technique, which represents maps and robot po...

Many learning systems search through a space of possible performance elements, seeking an element with high expected utility. As the task of nding the globally optimal element is usually intractable, many practical learning systems use hill-climbing to nd a local optimum. Unfortunately, even this is diicult, as it depends on the distribution of problems, which is typically unknown. This paper a...