International Business Machines Corporation
GENERATIVE REASONING FOR SYMBOLIC DISCOVERY

Abstract:

Provide a background theory applicable to a scientific problem as input to a computerized generative reasoner, which in turn produces a plurality of provable conjectures applicable to the problem, based on the input. Provide the plurality of provable conjectures and a set of input training data to a computerized model inference engine, which fits the input training data to the plurality of provable conjectures to obtain at least one candidate symbolic model reflecting scientific laws associated with the problem. Reduce a search space of a computerized prediction module by providing to the computerized prediction module the at least one candidate symbolic model. Provide new data to the computerized prediction module, which searches in the reduced search space to make a prediction related to the problem based on the new data and the at least one candidate symbolic model.