The questions of how humans process uncertain information is important to the development of knowledge-based systems in term of both knowledge acquisition and knowledge representation. This article reviews three bodies of psychological research that address this question: human perception, human probabilistic and statistical judgement, and human choice behavior. The general conclusion is that human behavior under certainty is often suboptimal and sometimes even fallacious. Suggestions for knowledge engineers in detecting and obviating such errors are discussed. The requirements for a system designed to reduce the effects of human factors in the processing of uncertain knowledge are introduced.

Review of Expert Micros

Essentially a survey of the development of PC-based expert systems and a review of existing applications, languages, and shells, this book leaves many of the important questions unanswered. Essentially a survey of the development of PC-based expert systems and a review of existing applications, languages, and shells, this book leaves many of the important questions unanswered.

Essentially a survey of the development of PC-based expert systems and a review of existing applications, languages, and shells, this book leaves many of the important questions unanswered.

The Oak Ridge National Laboratory (ORNL) Center for Engineering Systems Advanced Research (CESAR) is a national center for multidisciplinary long-range research and development (R&D) in machine intelligence and advanced control theory. Intelligent machines (including sensor-based robots) can be viewed as artificially created operational systems capable of autonomous decision making and action. One goal of the research is autonomous remote operations in hazardous environments. This review describes highlights of CESAR research through 1986 and alludes to future plans.

IEEE Computer, pages 100-109, January

Large engineering projects, such as the engineering development of computers, involve a large number of activities and require cooperation across a number of departments. Due to technological and market uncertainties, these projects involve the management of a large number of changes. The Callisto project was born out of realization that the classical approaches to project management do not provide sufficient functionally to manage large engineering projects. Callisto was initiated as a research effort to explore project scheduling, control and configuration problems during the engineering prototype development of large computer systems and to devise intelligent project management tools that facilitate the documentation of project management expertise and its reuse from one project to another. In the first phase of the project, rule-based prototypes were used to build quick prototypes of project management expertise and the project management knowledge required to support expert project managers. In the second phase, the understanding of point solutions was used to capture the underlying models of project management in distributed project negotiations and comparative analysis. This article provides an overview of the problems, experiments, and the resulting models of project knowledge and constraint-directed negotiation.

This is the second part of a two-part article describing AI work at the NASA Johnson Space Center (JSC). In the Space Operations Directorate, these groups include (1) the Mission Planning and Analysis Division (MPAD) - Technology Development and Applications Branch, (2) the Spacecraft Software Division, and (3) the Systems Division - Systems Support Section. This second part of the article describes the AI work in the Space Operations Directorate. The first part of the article, published in the last week of AI Magazine, (7:1, Summer 1986) described the AI work in the Research and Engineering Directorate.

This article provides some basic assistance to entrepreneurs involved in artificial intelligence, offering a synthesis of standard business-planning and capital-raising practices. Three main areas are discussed: (1) developing a corporate strategy, (2) developing a business plan that works, and (3) approaching sources of capital.

Research and applications work in AI is being conducted by several groups at Johnson Space Center (JSC). In the Research and Engineering Directorate, these groups include (1) the Artificial Intelligence and Information Sciences Office, (2) the Simulation and Avionics Integration Division, (3) the Avionics Systems Division (ASD), and (4) the Tracking and Communications Division. In the Space Operations Directorate, these groups include (1) the Mission Planning and Analysis Division - Technology Development and Applications Branch, (2) the Spacecraft Software Division, and (3) the Systems Division-Systems Support Section. The first part of the article describes the AI work in Research and Engineering Directorate.