Automated Modeling of Complex Systems for Answering Prediction Questions (1995)
J. Rickel
The ability to answer prediction questions is crucial in science and engineering. A prediction question describes a physical system under hypothetical conditions and asks for the resulting behavior of specified variables. Prediction questions are typically answered by analyzing (e.g., simulating) a mathematical model of the physical system. To provide an adequate answer to a question, a model must be sufficiently accurate. However, the model must also be as simple as possible to ensure tractable analysis and comprehensible results. Ensuring a simple yet adequate model is especially difficult for complex systems, which include many phenomena that can be described at many levels of detail. While tools exist for analysis, modeling is a creative, time-consuming task performed by humans. We have designed algorithms for automatically constructing models to answer prediction questions, implemented them in a program called TRIPEL, and evaluated them in the domain of plant physiology. Given a prediction question and domain knowledge, TRIPEL builds the simplest differential-equation model that can adequately answer it and automatically passes the model to a simulator to generate the desired predictions. TRIPEL uses knowledge of the time scales on which processes operate to identify and ignore insignificant phenomena and choose quasi-static representations of fast phenomena. It also uses novel criteria and methods to choose a suitable system boundary, separating relevant subsystems from those that can be ignored. Finally, it includes a novel algorithm for efficiently searching through alternative levels of detail in a vast space of possible models. TRIPEL successfully answered plant physiology questions using a large, multipurpose, botany knowledge base (covering 300 processes and 700 plant properties) independently developed by a domain expert. Because its methods are domain-independent, TRIPEL should be equally useful in many areas of science and engineering
PhD Thesis, Department of Computer Sciences, University of Texas at Austin.