Clancey troubleshooting tutor for only 20 The cognitive modeling group provided would like to see alternative cognitive hours gained a proficiency equivalent strong advocacy for the use of models available within a system to that of trainees with 40 months cognitive modeling in building these rather than a single "correct" model (almost 4 years) on-the-job training systems. They argued for increased used to justify instruction.

Based on the experience in manufacturing production scheduling problems which the AI community has amassed over the last ten years, a workshop was held to provide a forum for discussion of the issues encountered in the design of AI-based scheduling systems. Several topics were addressed including : the relative virtues of expert system, deep method, and interactive approaches, the balance between predictive and reactive components in a scheduling system, the maintenance of convenient scheduling descriptions, the application of the ideas of chaos theory to scheduling, the state of the art in schedulers which learn, and the practicality and desirability of a set of benchmark scheduling problems. This article expands on these issues, abstracts the papers which were presented, and summarizes the lengthy discussions that took place.

We are developing a phoneme based.

The paper suggests a statistical framework for the parameter estimation problemassociated with unsupervised learning in a neural network, leading to an exploratory projection pursuit network that performs feature extraction, or dimensionality reduction.

In the development of an image segmentation system for real time image processing applications, we apply the classical decision analysis paradigmby viewing image segmentation as a pixel classifica.

We have done an empirical study of the relation of the number of parameters (weights) in a feedforward net to generalization performance.

In this paper we compare regression and classification systems. A regression system can generate an output f for an input X, where both X and f are continuous and, perhaps, multidimensional. A classification system can generate an output class, C, for an input X, where X is continuous and multidimensional and C is a member of a finite alphabet. The statistical technique of Classification And Regression Trees (CART) was developed during the years 1973 (Meisel and Michalpoulos) through 1984 (Breiman el al).

A new learning algorithm, Learning by Choice of Internal Represetations (CHIR),was recently introduced. Whereas many algorithms reduce the learning process to minimizing a cost function over the weights, our method treats the internal representations as the fundamental entities to be determined. The algorithm applies a search procedure in the space of internal representations, and a cooperative adaptation of the weights (e.g. by using the perceptron learning rule). Since the introduction of its basic, single output version, theCHIR algorithm was generalized to train any feed forward network of binary neurons. Here we present the generalised version of the CHIR algorithm, and further demonstrate its versatility by describing how it can be modified in order to train networks with binary ( 1) weights. Preliminary tests of this binary version on the random teacher problem are also reported.

We describe a model that can recognize two-dimensional shapes in an unsegmented image, independent of their orientation, position, and scale. The model, called TRAFFIC, efficiently represents the structural relation between an object and each of its component features by encoding the fixed viewpoint-invariant transformation from the feature's reference frame to the object's in the weights of a connectionist network. Using a hierarchy of such transformations, with increasing complexity of features at each successive layer, the network can recognize multiple objects in parallel. An implementation ofTRAFFIC is described, along with experimental results demonstrating the network's ability to recognize constellations of stars in a viewpoint-invariant manner. 1 INTRODUCTION A key goal of machine vision is to recognize familiar objects in an unsegmented image, independent of their orientation, position, and scale. Massively parallel models have long been used for lower-level vision tasks, such as primitive feature extraction and stereo depth.

Distinctive electrocardiogram (EeG) patterns are created when the heart is beating normally and when a dangerous arrhythmia is present. Some devices which monitor the EeG and react to arrhythmias parameterize the ECG signal and make a diagnosis based on the parameters. The author discusses the use of a neural network to classify the EeG signals directly.