In this work, the problem of establishing routes through random MINs (RMINs) in a shared-memory, distributed computing system is addressed.

This paper considers the problem of expressing predicate calculus in connectionist networksthat are based on energy minimization. Given a firstorder-logic knowledgebase and a bound k, a symmetric network is constructed (like a Boltzman machine or a Hopfield network) that searches for a proof for a given query. If a resolution-based proof of length no longer than k exists, then the global minima of the energy function that is associated with the network represent such proofs. The network that is generated is of size cubic in the bound k and linear in the knowledge size. There are no restrictions on the type of logic formulas that can be represented.

Feedforward networks composed of units which compute a sigmoidal function ofa weighted sum of their inputs have been much investigated. We tested the approximation and estimation capabilities of networks using functions more complex than sigmoids. Three classes of functions were tested: polynomials, rational functions, and flexible Fourier series. Unlike sigmoids,these classes can fit nonmonotonic functions. They were compared on three problems: prediction of Boston housing prices, the sunspot count, and robot arm inverse dynamics. The complex units attained clearlysuperior performance on the robot arm problem, which is a highly nonmonotonic, pure approximation problem. On the noisy and only mildly nonlinear Boston housing and sunspot problems, differences among the complex units were revealed; polynomials did poorly, whereas rationals and flexible Fourier series were comparable to sigmoids. 1 Introduction

This is an extension of previous work on recognizing isolated digits.

Mark Fanty, Ronald A. Cole and Krist Roginski Center for Spoken Language Understanding Oregon Graduate Institute of Science and Technology 19600 N.W. Von Neumann Dr., Beaverton, OR 97006 Abstract A recognition system is reported which recognizes names spelled over the telephone with brief pauses between letters. The system uses separate neural networks to locate segment boundaries and classify letters. The letter scores are then used to search a database of names to find the best scoring name. The speaker-independent classification rate for spoken letters is89%. The system retrieves the correct name, spelled with pauses between letters, 91 % of the time from a database of 50,000 names. 1 INTRODUCTION The English alphabet is difficult to recognize automatically because many letters sound alike; e.g., BID, PIT, VIZ and F IS.

A CCO based signal processing IC that computes a fully parallel single quadrant vector-matrix multiplication has been designed and fabricated with a 2j..un CCO/CMOS process. The device incorporates an array of Charge Coupled Devices (CCO) which hold an analog matrix of charge encoding the matrix elements. Input vectors are digital with 1 - 8 bit accuracy.

Another class of nonlinear algorithms, exemplified by CART (Breiman, Friedman, Olshen, & Stone, 1984) and MARS (Friedman, 1990), generalizes classicaltechniques by partitioning the training data into non-overlapping regions and fitting separate models in each of the regions. These two classes of algorithms extendlinear techniques in essentially independent directions, thus it seems worthwhile to investigate algorithms that incorporate aspects of both approaches to model estimation. Such algorithms would be related to CART and MARS as multilayer neural networks are related to linear statistical techniques.

Following Simon's (1969) observation that complex behaviour may simply be the reflection of a complex environment a number of researchers (eg.

Many real-valued connectionist approaches to learning control realize exploration by randomness inaction selection. This might be disadvantageous when costs are assigned to "negative experiences" . The basic idea presented in this paper is to make an agent explore unknown regions in a more directed manner. This is achieved by a so-called competence map, which is trained to predict the controller's accuracy, and is used for guiding exploration. Based on this, a bistable system enables smoothly switching attention between two behaviors - exploration and exploitation - depending on expected costsand knowledge gain. The appropriateness of this method is demonstrated by a simple robot navigation task.

The focused gamma network is proposed as one of the possible implementations of the gamma neural model. The focused gamma network is compared with the focused backpropagation network and TDNN for a time series prediction problem, and with ADALINE in a system identification problem.