Inductive revision of quantitative process models

Abstract

Most research on computational scientific discovery has focused on developing an initial model, but an equally important task involves revising a model in response to new data. In this paper, we present an approach that represents candidate models as sets of quantitative processes and that treats revision as search through a model space which is guided by time-series observations and constrained by background knowledge cast as generic processes that serve as templates for the specific processes used in models. We demonstrate our system’s ability on three different scientific domains and associated data sets. We also discuss its relation to other work on model revision and consider directions for additional research.