Reasoning about nonlinear system identification

  • Authors:

  • Elizabeth Bradley;Matthew Easley;Reinhard Stolle

  • Affiliations:

  • Univ. of Colorado, Boulder;Rockwell Science Center, Palo Alto, CA;Xerox PARC, Palo Alto, CA

  • Venue:
  • Artificial Intelligence
  • Year:
  • 2001

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Abstract

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.