Seminar on Computational Learning and
Adaptation
Orchestrating Reasoning for Automated System Identification
Reinhard Stolle
Xerox PARC
Palo Alto, CA
www.ksl.stanford.edu/~stolle
I will describe the program PRET which automates system
identification, the process of finding a mathematical model of a
dynamical black-box system. PRET performs both structural
identification and parameter estimation by integrating several
reasoning modes: qualitative reasoning, qualitative simulation,
numerical simulation, geometric reasoning, constraint reasoning,
SLD-based resolution, reasoning with abstraction levels, declarative
meta-level control, and a simple form of truth maintenance.
Unlike other modeling programs that map structural or functional
descriptions to model fragments, PRET combines hypotheses about the
mathematics involved into candidate models that are intelligently
tested against observations about the target system.
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 of interest for the task
at hand. A key observation about the modeling process is the
following. Not only is the resulting model as abstract as possible
but also the reasoning during model construction takes place at the
highest possible level at any time.
I will present two examples of system identification tasks that this
automated modeling tool has successfully performed. The first, a
simple linear system, was chosen because it facilitates a brief and
clear presentation of PRET's features and reasoning techniques. In
the second example, a difficult real-world modeling task, I show how
PRET models a radio-controlled car used in the University of British
Columbia's soccer-playing robot project.
(Joint work with Elizabeth Bradley and Matt Easley, University of
Colorado.)
Date: Thurs., Feb 22
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Time: 4:15-5:30PM
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Place: Cordura 100
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