TARGET: Rapid Capture of Process Knowledge

TARGET (Task Analysis/Rule Generation Tool) represents a new breed of tool that blends graphical process flow modeling capabilities with the function of a top-down reporting facility. Since NASA personnel frequently perform tasks that are primarily procedural in nature, TARGET models mission or task procedures and generates hierarchical reports as part of the process capture and analysis effort. Historically, capturing knowledge has proven to be one of the greatest barriers to the development of intelligent systems. Current practice generally requires lengthy interactions between the expert whose knowledge is to be captured and the knowledge engineer whose responsibility is to acquire and represent the expert's knowledge in a useful form. Although much research has been devoted to the development of methodologies and computer software to aid in the capture and representation of some types of knowledge, procedural knowledge has received relatively little attention. In essence, TARGET is one of the first tools of its kind, commercial or institutional, that is designed to support this type of knowledge capture undertaking. This paper will describe the design and development of TARGET for the acquisition and representation of procedural knowledge. The strategies employed by TARGET to support use by knowledge engineers, subject matter experts, programmers and managers will be discussed. This discussion includes the method by which the tool employs its graphical user interface to generate a task hierarchy report. Next, the approach to generate production rules for incorporation in and development of a CLIPS based expert system will be elaborated. TARGET also permits experts to visually describe procedural tasks as a common medium for knowledge refinement by the expert community and knowledge engineer making knowledge consensus possible. The paper briefly touches on the verification and validation issues facing the CLIPS rule generation aspects of TARGET. A description of efforts to support TARGET's interoperability issues on PCs, Macintoshes and UNIX workstations concludes the paper

Metacognitive Development and Conceptual Change in Children

There has been little investigation to date of the way metacognition is involved in conceptual change. It has been recognised that analytic metacognition is important to the way older children acquire more sophisticated scientific and mathematical concepts at school. But there has been barely any examination of the role of metacognition in earlier stages of concept acquisition, at the ages that have been the major focus of the developmental psychology of concepts. The growing evidence that even young children have a capacity for procedural metacognition raises the question of whether and how these abilities are involved in conceptual development. More specifically, are there developmental changes in metacognitive abilities that have a wholescale effect on the way children acquire new concepts and replace existing concepts? We show that there is already evidence of at least one plausible example of such a link and argue that these connections deserve to be investigated systematically

EXPERT SYSTEMS

In recent decades IT and computer systems have evolved rapidly in economic informatics field. The goal is to create user friendly information systems that respond promptly and accurately to requests. Informatics systems evolved into decision assisted systems, and such systems are converted, based on gained experience, in expert systems for creative problem solving that an organization is facing. Expert systems are aimed at rebuilding human reasoning on the expertise obtained from experts, stores knowledge, establishes links between knowledge, have the knowledge and ability to perform human intellectual activities. From the informatics development point of view, expert systems are based on the principle of the knowledge separation from the treating program. Expert systems simulate the human experts reasoning on knowledge available to them, multiply the knowledge and explain their own lines of reasoning.expert systems, artificial intelligence, knowledge, expertise

Cognitive apprenticeship : teaching the craft of reading, writing, and mathtematics

Includes bibliographical references (p. 25-27)This research was supported by the National Institute of Education under Contract no. US-NIE-C-400-81-0030 and the Office of Naval Research under Contract No. N00014-85-C-002

Eliciting Expertise

Since the last edition of this book there have been rapid developments in the use and exploitation of formally elicited knowledge. Previously, (Shadbolt and Burton, 1995) the emphasis was on eliciting knowledge for the purpose of building expert or knowledge-based systems. These systems are computer programs intended to solve real-world problems, achieving the same level of accuracy as human experts. Knowledge engineering is the discipline that has evolved to support the whole process of specifying, developing and deploying knowledge-based systems (Schreiber et al., 2000) This chapter will discuss the problem of knowledge elicitation for knowledge intensive systems in general