BUILDING AND REFINING A KNOWLEDGE BASE FOR SYNTHETIC ORGANIC-CHEMISTRY VIA THE METHODOLOGY OF INDUCTIVE AND DEDUCTIVE MACHINE LEARNING

The Stony Brook SYNCHEM system is a large knowledge-based domain-specific heuristic problem-solving program that is able to find valid synthesis routes for organic molecules of substantial interest and complexity without online guidance on the part of its user. In common with many such AI performance programs, SYNCHEM requires a substantial knowledge base to make it routinely useful, but as the designers of most of these programs have discovered, it is very difficult to engage domain experts to the long-term dedication and intensity of commitment necessary to create a production-quality knowledge base. ISOLDE and TRISTAN are machine learning programs that use large computer-readable databases of specific reaction instances as a source of training examples for algorithms designed to extract the underlying reaction schemata via inductive and deductive generalization. ISOLDE learns principally by inductive generalization, while TRISTAN makes use of a methodology that is primarily deductive, and which is usually described as explanation-based learning. Since the individual reaction entries in most com¬puter-readable databases are often haphazardly sorted and classified, a taxonomy program called BRANGÄNE has been written to partition the input databases into coherent reaction classes using the methodology of conceptual clustering. © 1990, American Chemical Society. All rights reserved.