Proceedings of the Annual Meeting of the Cognitive Science Society

Much previous work in developing computational models of scientific discovery has concentrated on the formation of basic laws. The important role played by additional assumptions in this process is a neglected research topic. W e argue that hypotheses about structure are an important source of such additional assumptions, and that knowledge of this type can be embodied in the notion of Informal Qualitative Models (IQMs). In this paper, we demonstrate that such models can be synthesised by applying a set of operators to the most fundamental model in a domain. Heuristics are employed to control this process, which forms the basis of an architecture for model-driven scientific discovery. Conventional data-driven discovery techniques can be integrated into this architecture, resulting in laws which depend crucially on the model that is applied to a problem. This approach is illustrated by an historical survey of eighteenth and nineteenth century solution chemistry, which focuses on the evolution of the models employed by scientists. A series of models are synthesised which reflect these historical developments, showing the importance of structural models both in understanding certain aspects of the scientific discovery process, and as a basis for practical discovery systems.