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

Content Provider	IEEE Xplore Digital Library
Author	Stronger, D. Stone, P.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci., Texas Univ., Austin, TX (Stronger, D.; Stone, P.)
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
Starting Page	474
Ending Page	480
File Size	181316
Page Count	7
File Format	PDF
ISBN	0769527280
ISSN	10823409
DOI	10.1109/ICTAI.2006.96
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Polynomials Autonomous agents Function approximation Supervised learning Training data Testing Mobile robots Noise robustness Nearest neighbor searches Legged locomotion
Content Type	Text
Resource Type	Article

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