However, extended object-oriented oday, object-oriented programming important and powerful programming Italy, Sweden, the United languages and systems have paradigm, especially for Kingdom, and the United States were been developed that are adequate to the development of complex systems, invited to the workshop. This article handle AI applications. AI, raised and the major points made programming, a case of objectoriented however, is looking for knowledge during the presentations of the eight programming that has a representation and programming papers in the workshop's four sessions. AI, does not satisfy distributed AI applications and uses constructs (for The workshop started with an requirements because it lacks representation, example, frames) and notions (for introduction by Ibrahim in which he communication, and organization. Ibrahim posed a to the object-based concurrent The one-day workshop entitled number of questions related to the programming paradigm to close the Objects and AI, held in Sydney, Australia, theme of the workshop and asked gap with distributed AI, such as the on 25 August 1991 in conjunction the participants to address some of introduction of more powerful object with the 1991 International these questions during their talks and representations, a social theory of Joint Conference on Artificial Intelligence, discussion.

The algorithm presented performs gradient descent on the weight space of an Artificial Neural Network (ANN), using a finite difference to approximate the gradient The method is novel in that it achieves a computational complexity similar to that of Node Perturbation, O(N3), but does not require access to the activity of hidden or internal neurons. This is possible due to a stochastic relation between perturbations at the weights and the neurons of an ANN. The algorithm is also similar to Weight Perturbation in that it is optimal in terms of hardware requirements when used for the training ofVLSI implementations of ANN's.

IR is typically a sigmoidal function such as (1.2) but other choices than (1.2) are possible and of interest.

IR is typically a sigmoidal function such as (1.2) but other choices than (1.2) are possible and of interest.

The algorithm presented performs gradient descent on the weight space of an Artificial Neural Network (ANN), using a finite difference to approximate the gradient The method is novel in that it achieves a computational complexity similar to that of Node Perturbation, O(N3), but does not require access to the activity of hidden or internal neurons. This is possible due to a stochastic relation between perturbations at the weights and the neurons of an ANN. The algorithm is also similar to Weight Perturbation in that it is optimal in terms of hardware requirements when used for the training ofVLSI implementations of ANN's.

These results show how combinatorial structure can be based on the spatial nature of networks, and not just on their emulation of logical structure.

IR is typically a sigmoidal function such as (1.2) but other choices than (1.2) are possible and of interest.

The algorithm presented performs gradient descent on the weight space of an Artificial Neural Network (ANN), using a finite difference to approximate the gradient The method is novel in that it achieves a computational complexitysimilar to that of Node Perturbation, O(N3), but does not require access to the activity of hidden or internal neurons. This is possible due to a stochastic relation between perturbations at the weights and the neurons of an ANN. The algorithm is also similar to Weight Perturbation in that it is optimal in terms of hardware requirements whenused for the training ofVLSI implementations of ANN's.

These devices are implanted and perform three types of actions: l.monitor the heart 2.to pace the heart 3.to apply high energy/high voltage electric shock 1bey sense the electrical activity of the heart through leads attached to the heart tissue. Two types of sensing are commooly used: Single Chamber: Lead attached to the Right Ventricular Apex (RVA) Dual Chamber: An additional lead is attached to the High Right Atrium (HRA). The actions performed by defibrillators are based on the outcome of a classification procedure based on the heart rhythms of different heart diseases (abnormal rhythms or "arrhythmias").

This paper is concerned with the problem of learning in networks where some or all of the functions involved are not smooth. Examples of such networks are those whose neural transfer functions are piecewise-linear and those whose error function is defined in terms of the 100 norm. Up to now, networks whose neural transfer functions are piecewise-linear have received very little consideration in the literature, but the possibility of using an error function defined in terms of the 100 norm has received some attention. In this paper we draw upon some recent results from the field of nonsmooth optimization (NSO) to present an algorithm for the non smooth case. Our motivation for this work arose out of the fact that we have been able to show that, in backpropagation, an error function based upon the 100 norm overcomes the difficulties which can occur when using the 12 norm. 1 INTRODUCTION This paper is concerned with the problem of learning in networks where some or all of the functions involved are not smooth.