Collaborating Authors

CYC: Using Common Sense Knowledge to Overcome Brittleness and Knowledge Acquisition Bottlenecks

AI Magazine

The recent history of expert systems, for example, highlights how constricting the brittleness and knowledge acquisition bottlenecks are. Moreover, standard software methodology (e.g., working from a detailed "spec") has proven of little use in AI, a field which by definition tackles ill-structured problems. How can these bottlenecks be widened? Attractive, elegant answers have included machine learning, automatic programming, and natural language understanding. But decades of work on such systems (Green et al., 1974; Lenat et al., 1983; Lenat & Brown, 1984; Schank & Abelson, 1977) have convinced us that each of these approaches has difficulty "scaling up" for want of a substantial base of real world knowledge.

CYC: A Midterm Report

AI Magazine

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.


AI Magazine

The majority of work in knowledge representation has dealt with the technicalities of relating predicate calculus to other formalisms and with the details of various schemes for default reasoning. There has almost been an aversion to addressing the problems that arise in actually representing large bodies of knowledge with content. However, deep, important issues must be addressed if we are to ever have a large intelligent knowledge-based program: What ontological categories would make up an adequate set for carving up the universe? What are the important facts and heuristics most humans today know about solid objects? In short, we must bite the bullet.

Physical Object Representation and Generalization: A Survey of Programs for Semantic-Based Natural Language Processing

AI Magazine

This article surveys a portion of the field of natural language processing. The main areas considered are those dealing with representation schemes, particularly work on physical object representation, and generalization processes driven by natural language understanding. The emphasis of this article is on conceptual representation of objects based on the semantic interpretation of natural language input. Six programs serve as case studies for guiding the course of the article. Within the framework of describing each of these programs, several other programs, ideas, and theories that are relevant to the program in focus are presented.