Reasoning about nonlinear system identification

摘要

System identification is the process of deducing a mathematical model of the internal dynamics of a system from observations of its outputs. The computer program Pret automates this process by building a layer of artificial intelligence (AI) techniques around a set of traditional formal engineering methods. Pret takes a generate-and-test approach, using a small, powerful meta-domain theory that tailors the space of candidate models to the problem at hand. It then tests these models against the known behavior of the target system using a large set of more-general mathematical rules. The complex interplay of heterogeneous reasoning modes that is involved in this process is orchestrated by a special first-order logic system that uses static abstraction levels, dynamic declarative meta control, and a simple form of truth maintenance in order to test models quickly and cheaply. Unlike other modeling tools—most of which use libraries to model small, well-posed problems in limited domains and rely on their users to supply detailed descriptions of the target system—Pret works with nonlinear systems in multiple domains and interacts directly with the real world via sensors and actuators. This approach has met with success in a variety of simulated and real applications, ranging from textbook systems to real-world engineering problems.