Peter Stone's Selected Publications

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Polynomial Regression with Automated Degree: A Function Approximator for Autonomous Agents

Daniel Stronger and Peter Stone. Polynomial Regression with Automated Degree: A Function Approximator for Autonomous Agents. International Journal on Artificial Intelligence Tools, 17(1):159–174, February 2008.
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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.

BibTeX Entry

@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>},
}

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