Review of Artificial Intelligence for Microcomputers: The Guide for Business Decision Makers

Letters to the Editor

The everyday problem-solving activity within the organization can be thought of as conducted by a network of experts knowledgeable about the products and the physical and paperwork processes that constitute the business, that is, the knowledge network. The focus of our attention has not been just at the factory level; we have been addressing the order-process cycle: marketing, sales, order administration, manufacturing, distribution, and field service. This loop is the product life cycle: marketing and new product requirements, design and manufacturing startup, and volume or steady-state manufacturing. In addition to an overview of this knowledge network, we feature DEC's newest system in order processing: the configuration-dependent sourcing (CDS) expert.

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.

Writing about AI has turned out to be a considerably more difficult undertaining than many people had suspected. This book, although the best general introduction to AI that I have seen, still contains inadequacies.

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 rapid advances in information technology are causing a fundamental change in the way we do our business. Within our manufacturing business today, various parts of the organization are " reasoning " about "engineered products." The everyday problem-solving activity within the organization can be thought of as conducted by a network of experts knowledgeable about the products and the physical and paperwork processes that constitute the business, that is, the knowledge network. The focus of our attention has not been just at the factory level; we have been addressing the order-process cycle: marketing, sales, order administration, manufacturing, distribution, and field service. This cycle can be thought of as outer loop of the knowledge network. Also, we recently began addressing the inner loop. This loop is the product life cycle : marketing and new product requirements, design and manufacturing startup, and volume or steady-state manufacturing. This article describes DEC's internal strategy for applying artificial intelligence (AI) to manufacturing processes and problems above the work-cell level. In addition to an overview of this knowledge network, we feature DEC's newest system in order processing : the configuration-dependent sourcing (CDS) expert. Project experience on this system, which deals with the assignment of fulfillment sites (factories) to line items in computer system orders, is also described.

Other interesting topics in superb, it still contains inadequacies. This statement is more this chapter include nonmonotonic reasoning and modal and a testament to how remarkably difficult it is to write an adequate intentional logic. Perhaps the most intriguing chapter is introductory AI text than it is a criticism of the job done "Memory Organization and Deduction," which touches by the authors. Because there really are no single volumes upon the topics of frame-based representation and deductive yet that provide a satisfactory introduction to AI, the best retrieval and introduces the time-order representation approaches way to approach the problem of selecting text material for an of temporal system analysis and time map management introductory AI course seems to be use a book such as this .