In ICRA-87, Vol. 1, pp. 1–6.

Chapman and Hall. See also: Influence diagrams for Bayesian decision analysis, European Journal of Operational Research, Volume 40, Issue 3, 15 June 1989, Pages 363–376 (http://www.sciencedirect.com/science/article/pii/0377221789904293). Bayesian Decision Analysis: Principles and Practice, Cambridge University Press, 2010 (https://books.google.com/books/about/Bayesian_Decision_Analysis.html?id=O1lXnQAACAAJ).

In Automatic Control—World Congress, 1987: Selected Papers from the 10th Triennial World Congress of the International Federation of Automatic Control, pp. 221–226.

Complex Systems, 1 (5), 995-1019.

Artificial Intelligence, 31, 355–395.

Computational Intelligence, 3 (2), 78-93.

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.