Multimodal Reasoning about Physical Systems

Abstract We present a knowledge representation and reasoning framework that integrates qualitative reasoning, qualitative simulation, numerical simulation, geometric reasoning, constraint reasoning, resolution, reasoning with abstraction levels, declarative meta-level control, and a simple form of truth maintenance. The framework is the core of PRET, a system identification program that automates the process of modeling physical systems. Introduction Models are powerful tools that are used to understand physical systems. The process of inferring an internal model from external observations of a system---often called system identification--is a routine and difficult problem faced by engineers in a variety of domains The program PaET (Bradley & Stolle 1996) automates both stages of the system identification process; its goal is to find a system of ODEs that models

Reasoning About Sensor Data for Automated System Identification

The computer program pret automatically constructs mathematical models of physical systems. A critical part of this task is automating the processing of sensor data. pret's intelligent data analyzer uses geometric reasoning to infer qualitative information from quantitative data; if critical variables are either unknown or cannot be measured, it uses delay-coordinate embedding to reconstruct the internal dynamics from the external sensor measurements. Successful modeling results for two sensor-equipped systems, a driven pendulum and a radio-controlled car, demonstrate the effectiveness of these techniques