Parallelizable optimization techniques are applied to the problem of learning in feedforward neural networks. In addition to having superior convergenceproperties, optimization techniques such as the Polak Ribiere method are also significantly more efficient than the Backpropagation algorithm.These results are based on experiments performed on small boolean learning problems and the noisy real-valued learning problem of handwritten character recognition. 1 INTRODUCTION The problem of learning in feedforward neural networks has received a great deal of attention recently because of the ability of these networks to represent seemingly complex mappings in an efficient parallel architecture. This learning problem can be characterized as an optimization problem, but it is unique in several respects. Function evaluation is very expensive. However, because the underlying network is parallel in nature, this evaluation is easily parallelizable. In this paper, we describe the network learning problem in a numerical framework and investigate parallel algorithms for its solution. Specifically, we compare the performance of several parallelizable optimization techniques to the standard Back-propagation algorithm. Experimental results show the clear superiority of the numerical techniques. 2 NEURAL NETWORKS A neural network is characterized by its architecture, its node functions, and its interconnection weights. In a learning problem, the first two of these are fixed, so that the weight values are the only free parameters in the system.

Alternative suggestions The call for papers for AAAI-90 requires 68 Willow Road, Menlo Park, CA were discussed. Policy changes each author to specify his or her topic 94025.

Commonsense reasoning about the physical world, as exemplified by "Iron sinks in water" or "If a ball is dropped it gains speed," will be indispensable in future programs. We argue that to make such predictions (namely, envisioning), programs should use abstract entities (such as the gravitational field), principles (such as the principle of superposition), and laws (such as the conservation of energy) of physics for representation and reasoning. These arguments are in accord with a recent study in physics instruction where expert problem solving is related to the construction of physical representations that contain fictitious, imagined entities such as forces and momenta (Larkin 1983). We give several examples showing the power of physical representations.

The A.T. Nonmonotonic Workshop multisubmission paper policy by Anderson Memorial Scholarship Program The third international workshop on IJCAI was deferred until the of the American Indian Science August meeting.

This article records the results of an experiment in which a survey of AI and expert systems (ES) literature was attempted using Science Citation Indexes. The survey identified a sample of authors and institutions that have had a significant impact on the historical development of AI and ES. However, it also identified several glaring problems with using Science Citation Indexes as a method of comprehensively studying a body of scientific research. Accordingly, the reader is cautioned against using the results presented here to conclude that author A is a better or worse AI researcher than author B.

An artificial laboratory is a hypothetical computing environment of the future that would integrate mathematical and statistical tools with AI methods to assist in computer modeling and simulation. An integrated approach of this kind has great potential for accelerating the rate of scientific discovery.

Hutchins not only presents machine translation research (such as problems of machine translation It is the theories, algorithms, and designs practical versus theoretical, empirical also not clear that the AI philosophy but also the history, goals, assumptions, versus perfectionist, and direct versus of understanding and meaning (p 327) and constraints of each project.

Hutchins not only presents machine translation research (such as problems of machine translation It is the theories, algorithms, and designs practical versus theoretical, empirical also not clear that the AI philosophy but also the history, goals, assumptions, versus perfectionist, and direct versus of understanding and meaning (p 327) and constraints of each project.

Hutchins not only presents machine translation research (such as problems of machine translation It is the theories, algorithms, and designs practical versus theoretical, empirical also not clear that the AI philosophy but also the history, goals, assumptions, versus perfectionist, and direct versus of understanding and meaning (p 327) and constraints of each project.

Hutchins not only presents machine translation research (such as problems of machine translation It is the theories, algorithms, and designs practical versus theoretical, empirical also not clear that the AI philosophy but also the history, goals, assumptions, versus perfectionist, and direct versus of understanding and meaning (p 327) and constraints of each project.