machine learning

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.

Developing approaches for learning models of nonlinear, dynamic systems from time-series data.

We review the paradigm of inductive process modeling and examine its application to human physiology.

In the past, inductive process modeling systems have been limited to data sets that recorded change over time, but many interesting problems involve both spatial and temporal dynamics. To meet this challenge, we introduce SCISM, an integrated intelligent system which solves the task of inducing process models that account for spatial and temporal variation.

Research on transfer learning attempts to apply previously acquired knowledge to new learning tasks. Working within this area, we report on a procedure that learns and transfers constraints in the context of inductive process modeling.

We report on a system for inductive process modeling that uses structural constraints to reduce its search through the space of candidate models and to produce ones that human scientists find plausible.

We report some challenges encountered in developing Prometheus, a software environment that supports the construction and revision of explanatory scientific models. Our responses to these challenges include the use of quantitative processes, to encode models and background knowledge, and the combination of AND/OR search through a space of model structures with gradient descent to estimate parameters.

For the past few years, we have been developing computational tools to aid scientists in constructing process models of complex systems. This work integrates ideas from artificial intelligence, simulation environments, machine learning, and human–computer interaction to represent, simulate, and discover models that explain observations while remaining consistent with knowledge about a domain.

We discuss a mechanism for transfer learning in the context of inductive process modeling.