We present a methodological framework enabling a detailed description ofthe performance of Hopfield-like attractor neural networks (ANN) in the first two iterations. Using the Bayesian approach, wefind that performance is improved when a history-based term is included in the neuron's dynamics. A further enhancement of the network's performance is achieved by judiciously choosing the censored neurons (those which become active in a given iteration) onthe basis of the magnitude of their post-synaptic potentials. Thecontribution of biologically plausible, censored, historydependent dynamicsis especially marked in conditions of low firing activity and sparse connectivity, two important characteristics of the mammalian cortex. In such networks, the performance attained ishigher than the performance of two'independent' iterations, whichrepresents an upper bound on the performance of history-independent networks.

The Bayesian "evidence" approximation has recently been employed to determine the noise and weight-penalty terms used in back-propagation. This paper shows that for neural nets it is far easier to use the exact result than it is to use the evidence approximation. Moreover, unlike the evidence approximation,the exact result neither has to be re-calculated for every new data set, nor requires the running of computer code (the exact result is closed form). In addition, it turns out that the evidence procedure's MAPestimate for neural nets is, in toto, approximation error. Another advantage of the exact analysis is that it does not lead one to incorrect intuition, like the claim that using evidence one can "evaluate different priors in light of the data". This paper also discusses sufficiency conditions for the evidence approximation to hold, why it can sometimes give "reasonable" results, etc.

We are interested in the use of analog neural networks for recognizing visualobjects. Objects are described by the set of parts they are composed of and their structural relationship. Structural modelsare stored in a database and the recognition problem reduces to matching data to models in a structurally consistent way.The object recognition problem is in general very difficult in that it involves coupled problems of grouping, segmentation and matching. We limit the problem here to the simultaneous labelling ofthe parts of a single object and the determination of analog parameters. This coupled problem reduces to a weighted match problem in which an optimizing neural network must minimize E(M,p) LO'i MO'i WO'i(p), where the {MO'd are binary match variables for data parts i to model parts a and {Wai(P)} are weights dependent on parameters p .

First, the membership functions and an initial rule representation are learned; second, the rules are compressed as much as possible using information theory; and finally, a computational networkis constructed to compute the function value. This system is applied to two control examples: learning the truck and trailer backer-upper control system, and learning a cruise control systemfor a radio-controlled model car. 1 Introduction Function approximation is the problem of estimating a function from a set of examples ofits independent variables and function value. If there is prior knowledge of the type of function being learned, a mathematical model of the function can be constructed and the parameters perturbed until the best match is achieved. However, ifthere is no prior knowledge of the function, a model-free system such as a neural network or a fuzzy system may be employed to approximate an arbitrary nonlinear function. A neural network's inherent parallel computation is efficient for speed; however, the information learned is expressed only in the weights of the network. The advantage of fuzzy systems over neural networks is that the information learnedis expressed in terms of linguistic rules. In this paper, we propose a method for learning a complete fuzzy system to approximate example data.

Recently, both connectionist and symbolic methods have been developed for biasing learning with prior knowledge lFu,1989; Towell et a/., 1990; Ourston and Mooney, 1990]. Most ofthese methods revise an imperfect knowledge base (usually obtained from a domain expert) to fit a set of empirical data. Some of these methods have been successfully applied to real-world tasks, such as recognizing promoter sequences in DNA [Towell et ai., 1990; Ourston and Mooney, 1990]. The results demonstrate that revising an expert-given knowledge base produces more accurate results than learning from training data alone. Inthis paper, we describe the RAPTURE system (Revising Approximate 107 108 Mahoney and Mooney Probabilistic Theories Using Repositories of Examples), which combines connectionist andsymbolic methods to revise both the parameters and structure of a certainty-factor rule base. 2 The Rapture Algorithm

The First International Workshop on Rough Sets: State of the Art and Perspectives was held on 2-4 September 1992 in Kiekrz, Poland. To stimulate the discussion, the participation was limited to 40 researchers who are involved in fundamental research in rough set theory and its extensions, logic for approximate reasoning, machine learning, knowledge representation and transfer, and applications of rough set methodology. The workshop focused primarily on applications of the basic idea of the approximate definition of a set and its consequences in other areas of science and engineering. Applications discussed at the workshop included machine learning, medical diagnosis, fault detection, medical image processing, neural net training, database organization, drug research, and digital circuit design.

The First International Workshop on Rough Sets: State of the Art and Perspectives was held on 2-4 September 1992 in Kiekrz, Poland. To stimulate the discussion, the participation was limited to 40 researchers who are involved in fundamental research in rough set theory and its extensions, logic for approximate reasoning, machine learning, knowledge representation and transfer, and applications of rough set methodology. The workshop focused primarily on applications of the basic idea of the approximate definition of a set and its consequences in other areas of science and engineering. Applications discussed at the workshop included machine learning, medical diagnosis, fault detection, medical image processing, neural net training, database organization, drug research, and digital circuit design.

My Ph.D. dissertation describes PAGODA (probabilistic autonomous goal-directed agent), a model for an intelligent agent that learns autonomously in domains containing uncertainty. The ultimate goal of this line of research is to develop intelligent problem-solving and planning systems that operate in complex domains, largely function autonomously, use whatever knowledge is available to them, and learn from their experience. PAGODA was motivated by two specific requirements: The agent should be capable of operating with minimal intervention from humans, and it should be able to cope with uncertainty (which can be the result of inaccurate sensors, a nondeterministic environment, complexity, or sensory limitations). I argue that the principles of probability theory and decision theory can be used to build rational agents that satisfy these requirements.

The camera is mounted on a rotating table that allows it to turn 360 degrees independently of robot motion. Interestingly, the two teams processor (Z80) controls the robot's used vastly different approaches in the design wheel speed and direction. 's software design is hierarchical in The final scores for the robots, based solely structure. At the top level is a supervising on competition-day performance, constitute planning system that decides when to call only a rough evaluation of the merits of the subordinate modules for movement, vision, various systems. This article provides a technical or the recalibration of the robot's position.

A short overview of artificial intelligence and its relationship with fuzzy logic is provided. We emphasize the role fuzzy logics can play in extending some of the models of Artificial Intelligence.