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Summary: HUMAN-INSPIRED ROBOTIC FORGETTING: FILTERING TO IMPROVE
ESTIMATION ACCURACY
Sanford T. Freedman and Julie A. Adams
Electrical Engineering and Computer Science
Vanderbilt University
Nashville TN USA
sandytf@alumni.usc.edu and julie.a.adams@vanderbilt.edu
ABSTRACT
Perfect memory and recall provides a mixed blessing.
While flawless recollection of episodic data allows for
increased reasoning, photographic memory can hinder a
robot's ability to operate in real-time dynamic environ-
ments. Human-inspired forgetting methods may enable
robotic systems to rid themselves of out-dated, irrelevant,
and erroneous data. This paper presents the ActSimple al-
gorithm and an associated experimental analysis. The Act-
Simple algorithm is a novel approach to improving robotic
performance by filtering data available to existing algo-
rithms. The experimental analysis tested the effectiveness
of five forgetting algorithms in a WiFi signal strength es-
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