Toward a Computational Model of Hypothesis Formation and Model Building in Science

We present our two-part approach to computational scientific discovery. The first part is a heuristic search through the space of hypotheses to find good hytheses. The second stage is a heuristic search through the space of sets of hypotheses to find coherent models. Both of these heuristic searches strive for four criteria: empirical support, simplicity, novelty and utility. Four types of domain knowledge inform both searches: transforms, taxonomies, sequences and default models. We mention several experimental results that suggest the viability of our approach.

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