knowledge representation

We argue that free-form drawings facilitate the scientific modeling process and analyze the relationship between free-form drawings and formally encoded models. We then suggest how to exploit these relationships to develop a modeling environment that supports a tighter integration between conceptual and detailed modeling.

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.

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.

To determine whether anomaly-driven approaches to discovery produce more accurate models than the standard approaches, we built a program called Kalpana. We also used Kalpana to explore means for identifying those anomaly resolutions that are acceptable to domain experts.