An expert system for formulating lubricating oils

Abstract

The formulation of lubricants for automotive engines involves the highest levels of human decision-making. A project investigating the application of AI techniques to support this activity has proved to be a challenging exercise. The aim is not to replace formulators, but to automate only those aspects of their activity where it is reasonably practicable to do so. The basic approach taken is to view formulation as a hierarchical planning activity, with deep knowledge represented using a causal model. Formulation decisions are made by rule-bases which mix heuristic knowledge and causal reasoning, together with the facility for formulators to enter their own decisions. Decisions are represented as constraints and alternative decision paths are maintained. Representation of knowledge in a manner accessible to the end users is a key issue, with substantial amounts of the construction and maintenance of the knowledge base handled by the formulators themselves. Problems in the interpretation of statements about such a complex domain have been highlighted. The formulation system integrates with existing information technology, especially databases. Whilst complete automatic formulation is viewed as technically feasible, the support of formulators in more routine matters is seen as the only practicable means of applying AI technology to lubricant formulation.