A knowledge-based expert system for scheduling of airborne astronomical observations

The Kuiper Airborne Observatory Scheduler (KAOS) is a knowledge-based expert system developed at NASA Ames Research Center to assist in route planning of a C-141 flying astronomical observatory. This program determines a sequence of flight legs that enables sequential observations of a set of heavenly bodies derived from a list of desirable objects. The possible flight legs are constrained by problems of observability, avoiding flyovers of warning and restricted military zones, and running out of fuel. A significant contribution of the KAOS program is that it couples computational capability with a reasoning system

A Language for Rule-based Systems

Expert systems are proliferating in many situations in which it is important to capture expertise in a computer system. This type of system is useful in situations in which human expertise is expensive or difficult to obtain or in which the operating environment is too dangerous for a person. Expert systems are used to address the following categories of problems: interpretation, prediction, diagnosis, design, planning, monitoring, debugging, repair, instruction, and control. [Hayes-Roth] Expert system have now moved out of the laboratory and are being used in production environments. Herein lies the problem addressed by this research. Expert systems have traditionally been used in a research environment in which the software engineering of the product is not particularly important. Production environments are much more demanding. The quality necessary for continual use and abuse is not generally built into research quality expert systems. The problem is further exacerbated when an expert system is to be embedded in an autonomous system for which human interaction is difficult. (For example, an expert system could be used to drive a robot in a hazardous environment. If the expert system fails, it may not be easy for a human to reach the robot for repair.) Quality in these situations is vital

Exploiting the knowledge engineering paradigms for designing smart learning systems

Knowledge engineering (KE) is a subarea of artificial intelligence (AI). Recently, KE paradigms have become more widespread within the fields of smart education and learning. Developing of Smart learning Systems (SLS) is very difficult from the technological perspective and a challenging task. In this paper, three KE paradigms, namely: case-based reasoning, data mining, and intelligent agents are discussed. This article demonstrates how SLS can take advantage of the innovative KE paradigms. Therefore, the paper addresses the pros of such smart computing approaches for the industry of SLS. Moreover, we concentrate our discussion on the challenges faced by knowledge engineers and software developers in developing and deploying efficient and robust SLS. Overall, this study introduces the reader the KE techniques, approaches and algorithms currently in use and the open research issues in designing the smart learning systems.Инженерия знаний (ИЗ) – это подобласть искусственного интеллекта (ИИ). В последнее время парадигмы ИЗ и умных вычислений получают все более широкое распространение в сфере умного образования и обучения. Разработка систем умного обучения (СУО) является очень трудной с технологической точки зрения и сложной задачей. В данной статье мы изучили три парадигмы ИЗ, а именно рассуждения на основе прецедентов, интеллектуальный анализ данных и интеллектуальные агенты. Наше исследование указывает на то, что такие парадигмы могут эффективно использоваться для СУОІнженерія знань (ІЗ) – це пiдобласть штучного інтелекту (ШІ). Останнім часом парадигми ШІ та розумних обчислень отримують все більш широке поширення в сферi розумної освіти i навчання. Розробка систем розумного навчання (СРН) є дуже важким з технологічної точки зору і складним завданням. У даній статті ми вивчили три парадигми ШІ, а саме міркування на основі прецедентів, інтелектуальний аналіз даних та інтелектуальні агенти. Наше дослідження вказує на те, що такі парадигми можуть ефективно використовуватися для СР

Audit and security issues with expert systems;

https://egrove.olemiss.edu/aicpa_guides/1016/thumbnail.jp

Board of Trustees Meeting Minutes, February 4-5, 1992

Minutes from the Wright State University Board of Trustees Meeting held on February 4-5, 1992

LCSH and PRECIS in Library and Information Science: A Comparative Study

This study aims to compare the performance of LCSH and PRECIS for the books published in 1987 in the field of library and information science (LIS) in order to investigate the strengths and weaknesses of each system. Subject headings and PRECIS strings assigned for 82 titles have been analyzed and the two major subject access systems have been compared regarding the number of entries, exhaustivity and specificity of the entries provided, the variety of subdivisions, and other qualitative features