home · mobile · calendar · defenses · 2000-2001 · 

Thesis Defense - Easley

Automating Input-Output Modeling of Dynamic Physical Systems
Matthew Easley
Computer Science PhD Candidate

System identification is the process of deducing a mathematical model of the internal dynamics of a black-box system from observations of its outputs. Input-output modeling, where one applies test signals to the system and observes their effects on its outputs, has a critical role in this process. This procedure is extremely difficult to automate.

Any artificial intelligence tool that takes an input-output approach to modeling nonlinear dynamic physical systems must represent and reason with many heterogeneous kinds of knowledge about the real world. Knowledge about a target system ranges from general mathematics that applies in all situations -- for example, theorems about differential equations -- to very specific knowledge that is only useful in limited circumstances, such as specific forms of friction in ball bearings. The challenges in automating the input-output modeling process are to smoothly incorporate these varying levels and types of knowledge and to apply the appropriate reasoning techniques at the right place and time. This thesis describes a knowledge representation and reasoning framework that solves these problems. This framework encapsulates knowledge that is critical to the generation phase of the model-building process: fundamental engineering tactics and representations for efficient model construction and testing, and a succinct and yet effective method for reasoning about the relationship between a system's inputs and its outputs. The model building knowledge resides in a small, powerful meta-domain theory that tailors the space of candidate models to the problem at hand.

The input-output knowledge is instantiated in an intelligent sensor/actuator control module, which uses a process termed qualitative bifurcation analysis to reason effectively about sensors and actuators and their interactions with the target system. These two paradigms work well together: meta-domains provide a compact and flexible way of representing domain knowledge; qualitative bifurcation analysis allows the modeler to generalize detailed information about specific instances of a target system into abstract classes of behaviors.

The context in which I demonstrated the power and utility of these ideas was the program PRET, which automates the system identification process by building a layer of artificial intelligence techniques around a set of traditional formal engineering methods. 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 interact directly with the real world via sensors and actuators. My knowledge representation and reasoning framework allows PRET to succeed in a variety of simulated and real applications, ranging from textbook systems to real-world engineering problems.

Committee: Elizabeth Bradley, Associate Professor (Chair)
Xiao-Chuan Cai, Associate Professor
Andrzej Ehrenfeucht, Professor
James Martin, Associate Professor
David Meyer, Department of Electrical and Computer Engineering
Department of Computer Science
University of Colorado Boulder
Boulder, CO 80309-0430 USA
May 5, 2012 (14:20)