Conclusions from the Commodity Expert Project
The goal of the commodity expert project was to develop a prototype program that would act as an intelligent assistant to a commodity market analyst. Since expert analysis must deal with very large, yet incomplete, data bases of unreliable facts about a complex world, the project would stringently test the applicability of Artificial Intelligence techniques. After a significant effort however, I am forced to the conclusion that an intelligent, real-world system of the kind envisioned is currently out of reach. Some of the difficulties were due to the size and complexity of the domain. As its true scale became evident, the available resources progressively appeared less adequate. The representation and reasoning problems that arose were persistently difficult and fundamental work is needed before the tools will be sufficient to engineer truly intelligent assistants. Despite these difficulties, perhaps even because of them, much can be learned from the project. To assist future applications projects, I explain in this report some of the reasons for the negative result, and also describe some positive ideas that were gained along the way. In doing so, I hope to convey the respect I have developed for the complexity of real-world domains, and the difficulty of describing the ways experts deal them.