• Classified by Topic • Classified by Publication Type • Sorted by Date • Sorted by First Author Last Name • Classified by Funding Source •
Polynomial Regression with Automated Degree: A Function Approximator for Autonomous Agents.
Daniel
Stronger and Peter Stone.
International Journal on Artificial Intelligence
Tools, 17(1):159–174, February 2008.
official
published version
[PDF]236.4kB [postscript]1.0MB
In order for an autonomous agent to behave robustly in a variety of environments, it must have the ability to learn approximations to many different functions. The function approximator used by such an agent is subject to a number of constraints that may not apply in a traditional supervised learning setting. Many different function approximators exist and are appropriate for different problems. This paper proposes a set of criteria for function approximators for autonomous agents. Additionally, for those problems on which polynomial regression is a candidate technique, the paper presents an enhancement that meets these criteria. In particular, using polynomial regression typically requires a manual choice of the polynomial's degree, trading off between function accuracy and computational and memory efficiency. Polynomial Regression with Automated Degree (PRAD) is a novel function approximation method that uses training data to automatically identify an appropriate degree for the polynomial. PRAD is fully implemented. Empirical tests demonstrate its ability to efficiently and accurately approximate both a wide variety of synthetic functions and real-world data gathered by a mobile robot.
@Article{IJAIT08-stronger,
author="Daniel Stronger and Peter Stone",
title="Polynomial Regression with Automated Degree: A Function Approximator for Autonomous Agents",
volume = "17",
journal = "International Journal on Artificial Intelligence Tools",
number = "1",
month = "February",
year = "2008",
pages = "159--174",
abstract =
"In order for an autonomous agent to behave robustly
in a variety of environments, it must have the
ability to learn approximations to many different
functions. The function approximator used by such
an agent is subject to a number of constraints that
may not apply in a traditional supervised learning
setting. Many different function approximators
exist and are appropriate for different
problems. This paper proposes a set of criteria for
function approximators for autonomous agents.
Additionally, for those problems on which polynomial
regression is a candidate technique, the paper
presents an enhancement that meets these criteria.
In particular, using polynomial regression typically
requires a manual choice of the polynomial's degree,
trading off between function accuracy and
computational and memory efficiency. Polynomial
Regression with Automated Degree (PRAD) is a novel
function approximation method that uses training
data to automatically identify an appropriate degree
for the polynomial. PRAD is fully
implemented. Empirical tests demonstrate its ability
to efficiently and accurately approximate both a
wide variety of synthetic functions and real-world
data gathered by a mobile robot.",
wwwnote={<a href="http://www.worldscinet.com/ijait/17/1701/S02182130081701.html">
official published version</a>},
}
Generated by bib2html.pl (written by Patrick Riley ) on Tue Nov 19, 2024 10:24:39