Contrary to many prevailing approaches to knowledge acquisition, Laps, our expert-interviewing software, begins by soliciting cases from the expert, but it does not end there. Laps begins with a case in the form of a sample solution path elicited from the domain expert. This sample solution path is refined by a process called dechunking, which facilitates finding a model of the expert's reasoning process. Once these tables have been set up, the expert is able to produce row after row on his own until a complete rule base is built.

The system, Hoist, performs fault diagnosis without the use of a repair expert or shallow rules. Its knowledge is coded directly from a structural specification of the Mark 45 lower hoist. In a mechanism like the lower hoist, the functional model must reason about forces, fluid pressures, and mechanical linkages; that is, it must reason about qualitative physics. Hypothetical reasoning, the process embodied in Hoist, has general utility in qualitative physics and reason maintenance.

Arthur Samuel (1901-1990) was a pioneer of artificial intelligence research. From 1949 through the late 1960s, he did the best work in making computers learn from their expe-rience. His vehicle for this work was the game of checkers.

For our purposes, the goal is to make robots that are as humanlike as possible. Now the question becomes, Could we develop these systems to the point where x/h and The question of whether a computer deep interconnections among mind x/r were used interchangeably. In this can think like a person is once again and body are the crux of the issue. Somewhat to my surprise, Two basic lines of reasoning are thing when we said that Mary or R2D2 this philosophical question used to support the notion that computers understands Proust or loves John. The more common x/r could equal x/h, we must look understanding.

This article describes a causal expert system based on hypothetical reasoning and its application to the maintenance of the lower hoist of a Mark 45 turret gun. The system, Hoist, performs fault diagnosis without the use of a repair expert or shallow rules. Its knowledge is coded directly from a structural specification of the Mark 45 lower hoist. The technology reported here for assisting the less experienced diagnostician differs considerably from normal rule-based techniques: It reasons about machine failures from a functional model of the device. In a mechanism like the lower hoist, the functional model must reason about forces, fluid pressures, and mechanical linkages; that is, it must reason about qualitative physics. Hoist technology can be directly applied to any exactly specified device for the modeling and diagnosis of single or multiple faults. Hypothetical reasoning, the process embodied in Hoist, has general utility in qualitative physics and reason maintenance.

The Association for the Advancement of Artificial Intelligence held its 1990 Spring Symposium Series on March 27-29 at Stanford University, Stanford, California. This article contains a short summary of seven of the nine symposia that were conducted: AI and Molecular Biology, AI in Medicine, Automated Abduction, Case Based Reasoning, and Knowledge-Based Environments for Teaching and Learning.

Automated knowledge-acquisition systems have focused on embedding a cognitive model of a key knowledge worker in their software that allows the system to acquire a knowledge base by interviewing domain experts just as the knowledge worker would. Two sets of research questions arise: (1) What theories, strategies, and approaches will let the modeling process be facilitated; accelerated; and, possibly, automated? If automated knowledge-acquisition systems reduce the bottleneck associated with acquiring knowledge bases, how can the bottleneck of building the automated knowledge-acquisition system itself be broken? (2) If the automated knowledge-acquisition system centers on having an effective cognitive model of the key knowledge worker(s), to what extent does this model account for and attempt to influence human bias in knowledge base rule generation? That is, humans are known to be subject to errors and cognitive biases in their judgment processes. How can an automated system critique and influence such biases in a positive fashion, what common patterns exist across applications, and can models of influencing behavior be described and standardized? This article answers these research questions by presenting several prototypical scenes depicting bias and debiasing strategies.

After explicating the need for a large commonsense knowledge base spanning human consensus knowledge, we report on many of the lessons learned over the first five years of attempting its construction. We have come a long way in terms of methodology, representation language, techniques for efficient inferencing, the ontology of the knowledge base, and the environment and infrastructure in which the knowledge base is being built. We describe the evolution of Cyc and its current state and close with a look at our plans and expectations for the coming five years, including an argument for how and why the project might conclude at the end of this time.

The second workshop in two years on the integration of knowledge-based systems with conventional computer techniques in agriculture and natural resource management (NRM) was held 18-19 August 1989 in Detroit, Michigan, in conjunction with the Tenth International Joint Conference on Artificial Intelligence. The workshop drew scientists from the United States and Canada, working in disciplines from engineering to entomology in universities, government, and industry. Twenty-two papers were presented at the workshop, after which participants were asked to discuss several key questions about the development, delivery, and use of knowledge-based systems in solving problems in agriculture and NRM.

This article examines the near-term impact of expert system technology on work and the organization. From this analysis, a framework is constructed for viewing the impact of these technologies -- and technologies in general -- as a function of the technology itself; market realities; and personal, organizational, and societal values and policy choices. Two scenarios are proposed with respect to the application of this framework to expert systems. The second scenario posits that expert system diffusion will be pulled by, and will be a contributing factor toward, the evolution of the lean, flexible, knowledge-intensive, postindustrial organization.