ecology

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.

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.

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.

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

We introduce an approach for extracting constraints on process model construction. Results suggest that the learned constraints make sense ecologically and may provide insight into the nature of the modeled domain.

We introduce a method for representing process-based models that facilitates the discovery of structures that explain observed behavior. Using this approach, a modeler first encodes relevant ecological knowledge into a library of generic entities and processes, then instantiates these theoretical components, and finally assembles candidate models from these elements.

Borrett, S.R., Bridewell, W., Langley, P., & Arrigo, K.R. (2006). Process sensitivity analysis for ecological modeling. The Fifth International Conference on Ecological Informatics. Santa Barbara, CA.

Borrett, S.R., Bridewell, W., & Langley, P. (2006). Computational discovery of process models of aquatic ecosystems. Ninety-First Annual Meeting of the Ecological Society of America. Memphis, TN.

Research on inductive process modeling combines background knowledge with time-series data to construct explanatory models, but previous work has placed few constraints on search through the model space. We present an extended formalism that organizes process knowledge in a hierarchical manner and a system that carries out constrained search using this knowledge.