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 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.

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.

It (McDermott 1988), and the idea of generic also helps to explicitly focus on how to go tasks and task-specific architectures (Chandrasekaran from the knowledge level to the symbol or 1983). These various proposals are program level. I call this in-between level the obviously related to each other, which makes knowledge-use level. At the knowledge-use it desirable to construct a synthesis that combines level, we focus on issues such as how the their strengths. Such a synthesis is presented overall task will be decomposed into manageable here in the form of a componential subtasks, what ordering will be imposed framework. The framework stresses modularity on the tasks, what kind of access to knowledge and consideration of the pragmatic constraints will be needed (and, consequently, what of the domain.

This article examines the near-term impact of expert system technology on work and the organization. First, an approach is taken for forecasting the likely extent of the diffusion, or success, of the technology. Next, the case of advanced manufacturing technologies and their effects is considered. 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 first concludes that expert systems will have little impact on the nature of work and the organization. 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.

Jim when we want to define the class of should be justified by something Schmolze argued that if you think of people who work in specific institutions), other than the code implementing a sort of lingua franca for knowledge (2) when a concept definition the system. However, interpreting the representation, you can't be committed depends on the assertional properties two terms efficient and principled as to the difference between terminological of its instances (as with gray elephants, worst-case tractability and soundness and assertional knowledge for example), and (3) when and completeness with respect to the or even between roles and concepts.

To be considered exceptional, a textbook must satisfy three basic requirements. First, it must be authoritative, written by one with a broad range of experience in, and knowledge of, a subject. Second, it must effectively communicate to the reader, in the same manner in which a course instructor must be capable of imparting knowledge to students in a classroom. Third, it must stimulate the reader into thinking more deeply about the subject and into viewing it from fresh perspectives.

Many barriers exist today that prevent effective industrial exploitation of current and future AI research. These barriers can only be removed by people who are working at the scientific forefront in AI and know potential industrial needs. The Knowledge Processing Laboratory's research and development concentrates in the following areas: (1) natural language interfaces to knowledge-based systems and databases; (2) theoretical and experimental work on qualitative modeling and nonmonotonic reasoning for future knowledge-based systems; (3) application-specific language design, in particular, Prolog extensions; and (4) desi gn and analysis of neural networks. This article gives the reader an overview of the main topics currently being pursued in each of these areas.

Many barriers exist today that prevent effective industrial exploitation of current and future AI research. These barriers can only be removed by people who are working at the scientific forefront in AI and know potential industrial needs. The Knowledge Processing Laboratory's research and development concentrates in the following areas: (1) natural language interfaces to knowledge-based systems and databases; (2) theoretical and experimental work on qualitative modeling and nonmonotonic reasoning for future knowledge-based systems; (3) application-specific language design, in particular, Prolog extensions; and (4) desi gn and analysis of neural networks. This article gives the reader an overview of the main topics currently being pursued in each of these areas.