This paper describes an implemented computer program called PRET that automates the process of system identification: given hypotheses, observations, and specifications, it constructs an ordinary differential equation model of a target system with no other inputs or intervention from its user. The core of the program is a set of traditional system identification (SID) methods. A layer of artificial intelligence (AI) techniques built around this core automates the high-level stages of the identification process that are normally performed by a human expert. The AI layer accomplishes this by selecting and applying appropriate methods from the SID library and performing qualitative, symbolic, algebraic, and geometric reasoning on the user's inputs. For each supported domain (e.g., mechanics), the program uses a few powerful encoded rules (e.g., force balances) to combine hypotheses into models. A custom logic engine checks models against observations, using a set of encoded domain-independent mathematical rules to infer facts about both, modulo the resolution inherent in the specifications, and then searching for contradictions. The design of the next generation of this program is also described in this paper. In it, discrepancies between sets of facts will be used to guide the removal of unnecessary terms from a model. Power-series techniques will be exploited to synthesize new terms from scratch if the user's hypotheses are inadequate, and sensors and actuators will allow the tool to take an input-output approach to modeling real physical systems.
Full paper in postscript (290KB) and pdf(260KB).