We describe a neural network, called RufeNet, that learns explicit, symbolic condition-action rules in a formal string manipulation domain. of the domain,RuleNet discovers functional categories over elements and, at various points during learning, extracts rules that operate on these categories. The rules are then injected back into RuleNet and in a process called iterative projection. By incorporatingtraining continues, rules in this way, RuleNet exhibits enhanced learning and generalization performance over alternative neural net approaches. By integrating symbolic rule learning and subsymbolic category learning, RuleNet has capabilities that go beyond a purely symbolic system. We show how this architecture can be applied to the problem of case-role assignment in natural language processing, yielding a novel rule-based solution.

An effective to develop an abstract UI model which solution to this problem must have three encompasses the essence of those systems.

The workshop "Advances in Interfacing Production Systems with the Real World" was designed to bring together researchers from around the world to focus on the problem of integrating production systems into industrial environments. It was held on 25 August 1991 in Sydney, Australia, in conjunction with the Twelfth International Joint Conference on Artificial Intelligence (IJCAI-91). Nine papers were accepted for the proceedings, and six of them were discussed at the workshop.

The workshop "Advances in Interfacing Production Systems with the Real World" was designed to bring together researchers from around the world to focus on the problem of integrating production systems into industrial environments. It was held on 25 August 1991 in Sydney, Australia, in conjunction with the Twelfth International Joint Conference on Artificial Intelligence (IJCAI-91). Nine papers were accepted for the proceedings, and six of them were discussed at the workshop.

Decision analysis and expert systems are technologies intended to support human reasoning and decision making by formalizing expert knowledge so that it is amenable to mechanized reasoning methods. Despite some common goals, these two paradigms have evolved divergently, with fundamental differences in principle and practice. Recent recognition of the deficiencies of traditional AI techniques for treating uncertainty, coupled with the development of belief nets and influence diagrams, is stimulating renewed enthusiasm among AI researchers in probabilistic reasoning and decision analysis. We present the key ideas of decision analysis and review recent research and applications that aim toward a marriage of these two paradigms. This work combines decision-analytic methods for structuring and encoding uncertain knowledge and preferences with computational techniques from AI for knowledge representation, inference, and explanation. We end by outlining remaining research issues to fully develop the potential of this enterprise.

This article reports on experiments performed using a black-box simulation of a spacecraft. The goal of this research is to learn to control the attitude of an orbiting satellite. The space-craft must be able to operate with minimal human supervision. To this end, we are investigating the possibility of using adaptive controllers for such tasks. Laboratory tests have suggested that rule-based methods can be more robust than systems developed using traditional control theory. The BOXES learning system, which has already met with success in simulated laboratory tasks, is an effective design framework for this new exercise.

To cite or link to this reference: http://hdl.handle.net/10068/43975

Models of design processes provide guidance in the development of knowledge-based systems for design. The basis for such models comes from research in design theory and methodology as well as problem solving in AI. Three models are presented: decomposition, case-based reasoning, and transformation. Each model provides a formalism for representing design knowledge and experience in distinct and complementary forms.

Contrary to many prevailing approaches to knowledge acquisition, Laps, our expert-interviewing software, begins by soliciting cases from the expert, but it does not end there. Its uniqueness lies in the fact that it interweaves knowledge gathering, organizing, and testing. Laps begins with a case in the form of a sample solution path elicited from the domain expert. This sample solution path is refined by a process called dechunking, which facilitates finding a model of the expert's reasoning process. The model guides the determination of the structure of alternatives tables at an effective level of abstraction. Once these tables have been set up, the expert is able to produce row after row on his own until a complete rule base is built. A rule generator currently produces rules in Clips or M.1 syntax.

It (McDermott 1988), and the idea of generic also helps to explicitly focus on how to go tasks and task-specific architectures (Chandrasekaran from the knowledge level to the symbol or 1983). These various proposals are program level. I call this in-between level the obviously related to each other, which makes knowledge-use level. At the knowledge-use it desirable to construct a synthesis that combines level, we focus on issues such as how the their strengths. Such a synthesis is presented overall task will be decomposed into manageable here in the form of a componential subtasks, what ordering will be imposed framework. The framework stresses modularity on the tasks, what kind of access to knowledge and consideration of the pragmatic constraints will be needed (and, consequently, what of the domain.