The AI lab at the Courant Institute at New York University (NYU) is pursuing many different areas of artificial intelligence (AI), including natural language processing, vision, common sense reasoning, information structuring, learning, and expert systems. Other groups in the Computer Science Department are studying such AI-related areas as text analysis, parallel Lisp and Prolog, robotics, low-level vision, and evidence theory.

This article describes an effort to develop a knowledge-based financial marketing consultant system. Financial marketing is an excellent vehicle for both research and application in artificial intelligence (AI). This domain differs from the great majority of previous expert system domains in that there are no well-defined answers (in traditional sense); the goal here is to obtain satisfactory arguments to support the conclusions made. The experience gained in the initial prototyping effort is currently being used to further expert systems research and to develop an extensive system that ultimately can be used by the marketing organization.

The operations sheets generator (OPGEN) is an expert system that helps industrial engineers at the Hazeltine manufacturing and operations facilities plan the assembly of printed circuit boards. In this article, we describe the evolution of OPGEN from its initial development in the Hazeltine research laboratories to its routine use in an integrated manufacturing environment. We describe our approaches to the problem that occurred during the development, integration, and rehosting of OPGEN and provide some methodological guidelines to expert system builders who are concerned with the final delivery of an expert system.

Large engineering projects, such as the engineering development of computers, involve a large number of activities and require cooperation across a number of departments. 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.

The development of an online turbine generator diagnostic system is described from conception to initial field verification. The system is composed of a data center located in the power plant that collects data from online measurement devices and communicates these data to a centralized diagnostic facility in Orlando, Florida, where the actual diagnosis is done. The resulting diagnosis and recommended actions are transmitted to the power plant where they are displayed to the operator by the data center. The market-place need, initial approaches to the product, system field verification are described.

A new computer language for manufacturing is being used to link complex systems of equipment whose components are supplied by multiple vendors. The Cell Management Language (CML) combines computational tools from rule-based data systems, object-oriented languages, and new tools that facilitate language processing. These language tools, combined with rule processing, make it convenient to build new interpreters for interfacing and understanding a range of computer and natural languages; hence, CML is being used primarily to define other languages in an interpretive environment, that is, as a meta-interpreter. For example, in CML it is quite easy to build an interpreter for machine tool languages that can understand and generate new part programs.

Human schedulers are typically overburdened by the complexity of this task, and conventional computer-based scheduling systems consider only a small fraction of the relevent knowledge. This article describes research aimed at providing a framework in which all relevant scheduling knowledge can be given consideration during schedule generation and revision. Factory scheduling is cast as a complex constraint-directed activity, driven by a rich symbolic model of the factory environment in which various influencing factors are formalized as constraints. Two knowledge-based factory scheduling systems that implement aspects of this approach are described.

This article provides a discussion of factory design and an artificial intelligence (AI) approach to this problem. Major issues covered include knowledge acquisition and representation, design methodology, system architecture, and communication. The facilities design expert systems (FADES developed by the author is presented and described to illustrate issues in factory design.

The Parametric Interpretation Expert System (PIES) is a knowledge system for interpreting the parametric test data collected at the end of complex semiconductor fabrication processes. The system transforms hundreds of measurements into a concise statement of all the overall health of the process and the nature and probable cause of any anomalies. A key feature of PIES is the structure of the knowledge base, which reflects the way fabrication engineers reason causally about semiconductor failures. This structure permits fabrication engineers to do their own knowledge engineering, to build the knowledge base, and then to maintain it to reflect process modifications and operating experience.

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.