A Life Dedicated to the Science, Philosophy and Romanian Society

With outstanding contributions in Electronics, Informatics, and Philosophy, and as Professor, Researcher, and Manager, Acad. Mihai Drăgănescu is the most important encyclopedic personality of contemporary Romania. Educated in the nascent Romanian school of electronics, Acad. Mihai Drăgănescu creates a worldclass school of electronic devices and microelectronics. Envisioning the evolution of the modern society, becomes initiator and promoter of the informatics revolution in Romania, conceptually defining it and coordinating its development. Generalizing the concept of information, creates an original philosophy that leads to the development of a new type of science, called structural-phenomenological, with major implications for the understanding of the world and its future. First president of the Romanian Academy renaissance, he leads both its return to its role and traditional sources, and its renewal and adaptation to the evolution of the civilization. Promoter of the scientific and humanistic culture, brings back the deserved recognition to major personalities of the Romanian spirituality. Mentor and life model, he lightens and encourages many young generations with an extraordinary generosity. Any of these achievements would be enough to place Acad. Mihai Drăgănescu among the greatest Romanian personalities. Their combination, impressive through their diversity and unity, creates the image of a personality of a rare complexity and creativity

Building an adaptive agent to monitor and repair the electrical power system of an orbital satellite

Over several years we have developed a multistrategy apprenticeship learning methodology for building knowledge-based systems. Recently we have developed and applied our methodology to building intelligent agents. This methodology allows a subject matter expert to build an agent in the same way in which the expert would teach a human apprentice. The expert will give the agent specific examples of problems and solutions, explanations of these solutions, or supervise the agent as it solves new problems. During such interactions, the agent learns general rules and concepts, continuously extending and improving its knowledge base. In this paper we present initial results on applying this methodology to build an intelligent adaptive agent for monitoring and repair of the electrical power system of an orbital satellite, stressing the interaction with the expert during apprenticeship learning

Steps Toward Automating Knowledge Acquisition for Expert Systems

This paper presents a learning-based approach to the automation of knowledge acquisition for expert systems. An expert system is viewed as an explicit mooel of a human expert's competence and perfonnance. We distinguish three phases in the development of such a model. The fIrst one consists of defIning a framework for the mooel, in terms of a knowledge representation formalism and an associated problem solving methoo. The second phase consists of defIning a preliminary mooel that describes the basic concepts of the expertise domain. The last phase consists of incrementally extending and improving the domain model through learning from the human expert. The paper describes the learning system NeoDISCIPLE which illustrates the usefulness of six principles for automating the knowledge acquisition process: expert system building as a threephase mooeling of human expertise, understanding-based knowledge extension, knowledge acquisition through multistrategy learning, consistency-driven concept fonnation and refinement, closed-loop learning, and cooperation between the human expert and the learning system

DISCIPLE : une theorie, une methodologie et un systeme pour apprendre des connaissances expertes

CNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

Abstract In Y.Kodratoff (ed), Machine Learning- EWSL91, Springer-Verlag, 1991 A MULTISTRATEGY LEARNING APPROACH TO DOMAIN MODELING AND KNOWLEDGE ACQUISITION

This paper presents an approach to domain modeling and knowledge acquisition that consists of a gradual and goal-driven improvement of an incomplete domain model provided by a human expert. Our approach is based on a multistrategy learning method that allows a system with incomplete knowledge to learn general inference or problem solving rules from specific facts or problem solving episodes received from the human expert. The system will learn the general knowledge pieces by considering all their possible instances in the current domain model, trying to learn complete and consistent descriptions. Because of the incompleteness of the domain model the learned rules will have exceptions that are eliminated by refining the definitions of the existing concepts or by defining new concepts