Discovering Constraints for Inductive Process Modeling

Abstract

Scientists use two forms of knowledge in the construction of explanatory models: generalized entities and processes that relate them; and constraints that specify acceptable combinations of these components. Previous research on inductive process modeling, which constructs models from knowledge and time-series data, has relied on handcrafted constraints. In this paper, we report an approach to discovering such constraints from a set of models that have been ranked according to their error on observations. Our approach adapts inductive techniques for supervised learning to identify process combinations that characterize accurate models. We evaluate the method’s ability to reconstruct known constraints and to generalize well to other modeling tasks in the same domain. Experiments with synthetic data indicate that the approach can successfully reconstruct known modeling constraints. Another study using natural data suggests that transferring constraints acquired from one modeling scenario to another within the same domain considerably reduces the amount of search for candidate model structures while retaining the most accurate ones.