R. Stolle and E. Bradley, "Opportunistic modeling," Proceedings of the IJCAI (International Joint Conference on Artificial Intelligence) Workshop on Engineering Problems in Qualitative Reasoning, Nagoya Japan; August 1997


System identification - the process of inferring an internal model from external observations of a system - is a routine and difficult problem faced by engineers in a variety of domains. Typically, in the hierarchy from more-abstract to less-abstract models, the model of choice is the one that is just detailed enough to account for the properties and perspectives that are of interest for the task at hand. The main goal of the work described here was to design and implement a knowledge representation framework that allows a computer program to reason about physical systems and candidate models - ordinary differential equations (ODEs), specifically - in such a way as to find the right model at the right abstraction level as quickly as possible.

A key observation about the modeling process is the following. Not only is the resulting model the least complex of all possible ones, but also the reasoning during model construction takes place at the highest possible level at any time. Because of this, the knowledge representation framework was designed to allow easy formulation of knowledge and meta knowledge relative to various abstraction levels. The implemented framework is the core of PRET, an automatic modeling program that automates the system identification process.

We present two examples of system identification tasks that can be performed by PRET. The first example is a simple linear system that we have chosen for a brief and clear presentation of PRET's features and reasoning techniques. The second example is a real-world modeling task: We show how PRET models a radio-controlled car used in the University of British Columbia's soccer-playing robot project and discuss important research directions that arise from this real-world example.