Expert Systems
Review of Knowledge-Based Systems
The vendors Based Systems, 355 pp., and Volume 2, techniques. They are interesting of knowledge-based-systems development Knowledge Acquisition Tools for Expert and informative, particularly tools, for example, Inference, Systems, 343 pp., Academic Press, San "Generalization and Noise" by Y. IntelliCorp, Aion, AI Corp., and IBM, Diego, California, 1988), edited by B. Kodratoff and M. Manango, which would do well to pay heed to these R. Gaines and J. H. Boose, is an excellent discusses symbolic and numeric rule books because they point the way to collection of papers useful to both induction.
The Advanced Architectures Project
The Advanced Architectures Project at Stanford University's Knowledge Systems Laboratory seeks to gain higher performance for expert system applications through the design of new, innovative software and hardware architectures. This research concentrates particularly on the use of parallel machines to gain speedup and the design of the software to exploit emergent paral-lel hardware architectures. This article describes the project and details its goals and the work performed in the pursuance of these goals. A brief description is given of each of the project components, and a complete bibliography appears of the publications produced for the project.
In Defense of Reaction Plans as Caches
Universal plans address the tension between reasoned behavior and timely response by caching reactions for classes of possible situations. This technique reduces the average time required to select a response at the expense of the space required to store the cache-the classic time-space trade-off. In his article, Matthew Ginsberg argues from the time extreme and against the space extreme. Although I find both extremes undesirable, I defend an increase in space consumption.
Knowledge-Based System Applications in Engineering Design: Research at MIT
Sriram, Duvvuru, Stephanopoulos, George, Logcher, Robert, Gossard, David, Groleau, Nicholas, Serrano, David, Navinchandra, Dundee
Advances in computer hardware and software and engineering methodologies in the 1960s and 1970s led to an increased use of computers by engineers. AI techniques, in particular the knowledge-based system (KBS) technology, offer a methodology to solve these ill-structured design problems. In this article, we describe several research projects that utilize KBS techniques for design automation. These projects are (1) the Criteria Yielding, Consistent Labeling with Optimization and Precedents-Based System (CYCLOPS), which generates innovative designs by using a three-stage process: normal search, exploration, and adaptation; (2) the Concept Generator (CONGEN), which is a domain independent framework for conceptual or preliminary design; (3) Constraint Manager (CONMAN), which is a constraint-management system that performs the evaluation and consistency maintenance of constraints arising in design; (4) the distributed and integrated environment for computer-aided engineering (DICE), which facilitates coordination, communication, and control during the entire design and construction/manu-facturing phases; and (5) DESIGN-KIT, which can be envisioned as a new generation of computer-aided engineering environment for process-engineering applications.
On Interface Requirements for Expert Systems
Nevertheless, significant aspects of behavior and user expectation are peculiar to expert systems and their users. These considerations are discussed here with examples from an actual system. Guidelines for the behavior of expert systems and the responsibility of designers to their users are proposed.
Letters to the Editor.
Shortliffe, Edward H., Wilson, Kirk, Brender, David, Cott, Harold Van
These debates end by a culture for accommodating of the medical AI community, I feel I up merely as arguments in which its limited knowledge representations. Those of us in intelligence is). Depending such an extent that the limits of the medical AI have been highly sensitized upon what properties of human and computer system would no longer be to common misunderstandings artificial intelligence are stressed we a representational problem? We also encounter a general lack of of the relationship. Will we need to ascribe pleasure and realistic expectations regarding the The problem is that the models of pain to our computer experts?