CYC: Using common sense knowledge to overcome brittleness and knowledge acquistion bottlenecks

TLDR

Large software systems suffer from brittleness and high manpower demands, and expert systems have shown that these brittleness and knowledge‑acquisition bottlenecks are hard to overcome, especially in AI where ill‑structured problems limit the effectiveness of traditional software methods. The study investigates how to widen the brittleness and knowledge‑acquisition bottlenecks in software development. They find that machine learning, automatic programming, and natural language understanding all struggle to scale without a substantial base of real‑world knowledge.

Abstract

The major limitations in building large software have always been (a) its brittleness when confronted by problems that were not foreseen by its builders, and (by the amount of manpower required. 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 have convinced us that each of these approaches has difficulty scaling up for want a substantial base of real world knowledge.