The growing amount of scientific data has led to the increased use of computational discovery methods to understand and interpret them. However, most work has relied on knowledge-lean techniques like clustering and classification learning, which produce descriptive rather than explanatory models, and it has utilized formalisms developed in AI or statistics, so that results seldom make contact with current theories or scientific notations. In this talk, I present a new approach to computational discovery that encodes explanatory scientific models as sets of quantitative processes, simulates these models' behavior over time, incorporates background knowledge to constrain model construction, and induces these models from time-series data in a robust manner. I illustrate this framework on data and models from Earth science and microbiology, two domains in which explanatory process accounts occur frequently. In closing, I describe our progress toward an interactive software environment for the construction, evaluation, and revision of such explanatory scientific models.

This talk describes joint work with Kevin Arrigo, Stephen Bay, Lonnie Chrisman, Dileep George, Andrew Pohorille, Javier Sanchez, Dan Shapiro, and Jeff Shrager.