Using Stereotypes and Compactification of Observations to Improve Modeling of Other Agents
Date
2004-05-04
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Abstract
This paper investigates improvements to modeling other agents based
on observed situation-action pairs and the nearest-neighbor rule, which
suffers when dealing with very few or very many observations. Stereotype
models allow for good predictions of a modeled agent s behavior even after
few observations. To handle any adverse effects of stereotyping, periodic
reevaluation of the chosen stereotype and the potential to switch between
different stereotypes aids in dealing with very similar, but not identical,
stereotypes. Also, periodic reevaluation and the potential to switch from a
chosen stereotype to the original observation based modeling method aids in
dealing with agents that do not conform to any stereotype. Finally,
compactification of observation keeps the application of the original
modeling method efficient by reducing comparisons within the nearest neighbor
rule. <BR><BR>Our experiments within the OLEMAS system show that stereotyping
significantly improves cases where just using the original modeling performs
poorly. In addition, reevaluation and switching fortify stereotyping against
the potential risk of using an incorrect stereotype. In many cases,
compactification both increases the efficiency of using observed
situation-action pairs and improves predictions by filtering out misleading
observation. At times however, this filtering comes at the cost of
prediction accuracy by removing relevant observations.
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Computer Science