Jonathan L. Shapiro

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The ability to grow extra nodes is a potentially useful facility for a self-organising neural network. A network that can add nodes into its map space can approximate the input space more accurately, and often more parsimoniously, than a network with predefined structure and size, such as the Self-Organising Map. In addition, a growing network can deal with(More)
To navigate in unknown environments, mobile robots require the ability to build their own maps. A major problem for robot map building is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of cumulative drift errors. This paper introduces a fast, on-line algorithm for learning geometrically(More)
Recognising new or unusual features of an environment is an ability which is potentially very useful to a robot. This paper demonstrates an algorithm which achieves this task by learning an internal representation of 'normality' from sonar scans taken as a robot explores the environment. This model of the environment is used to evaluate the novelty of each(More)
The use of mobile robots for inspection tasks is an attractive idea. A robot can travel through environments that humans cannot, and can be trained to identify sensor perceptions that signify potential or actual problems without requiring human intervention. However, in many cases, the appearance of a problem can vary widely, and ensuring that the robot(More)
Mobile robots require the ability to build their own maps to operate in unknown environments. A fundamental problem is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of drift errors caused by wheel slippage. This paper introduces a fast, on-line method of learning globally consistent maps,(More)
A statistical mechanical formulation of the dyamics of genetic algorithms is described. This formulation allows the derivation of equations which predict the distributions of tness with the population at one generation in terms of the distribution at the previous generation. The eeects of selection are problem independent, and the formulation predicts an(More)
In this paper a novelty lter is introduced which allows a robot operating in an unstructured environment to produce a self-organised model of its surroundings and to detect deviations from the learned model. The environment is perceived using the robot's 16 sonar sensors. The algorithm produces a novelty measure for each sensor scan relative to the model it(More)
The ability of a robot to detect and respond to changes in its environment is potentially very useful , as it draws attention to new and potentially important features. We describe an algorithm for learning to filter out previously experienced stimuli to allow further concentration on novel features. The algorithm uses a model of habituation, a biological(More)
Estimation of Distribution Algorithms (EDAs) are a class of evolutionary algorithms that use machine learning techniques to solve optimization problems. Machine learning is used to learn probabilistic models of the selected population. This model is then used to generate next population via sampling. An important phenomenon in machine learning from data is(More)