Creating a 30-Million-Rule System: MCC and Cycorp

Hard-won discoveries led to early expert systems (ESs) successes, then overhyping, and then disillusionment. The bottleneck was infrastructure: limited expressivity representation languages, inefficient inference engines, inadequate ontologies, as well as lack of common-sense, general theories of the world, and argumentation and context mechanisms. At Microelectronics and Computer Technology Corporation and then at Cycorp, we have systematically codified much of the “obvious” knowledge of the world that one rarely articulates since “everyone” of course already knows it. Lack of that solid infrastructure limits AIs' trustworthiness: they make mistakes no human would make, and they cannot explain their reasoning. This is the story of my ESs experience and how 50 years of lessons learned led my team to steadily and successfully construct an enormous knowledge-based system that avoids such brittleness.