Abstract: In this paper, we propose two improvements to modeling other agents
based on Observed Situation-Action Pairs and the Nearest Neighbor Rule –
reevaluative stereotyping with switching and compactification of observations
through kd-tree structuring and the Pseudo-Approximate Nearest Neighbor search.
On the one hand, tentative stereotype models allow for good predictions of a
modeled agent's behavior even after few observations. Periodic reevaluations of
the chosen stereotype and of the stereotyping process itself, in addition to
the potential for switching between different stereotypes or to the observation
based model aids in dealing with very similar (but not identical) stereotypes
and agents that do not conform to any stereotype. On the other hand, reducing
comparisons for the Nearest Neighbor Rule by observation compactification keeps
the application of the model efficient even after many observations have been
made. Our experiments show that tentative stereotyping significantly improves
cases in which the original method performs badly and that reevaluations and
switching fortify stereotyping against the potential risk of using an incorrect
stereotype. For compactification, our experiments show that using the kd-tree
for compactifying observations and the Pseudo-Approximate Nearest Neighbor
search for retrieving a Nearest Neighbor improves modeling efficiency when
observations are abundant, but is sometimes coupled with a loss of
accuracy.
Keywords: Opponent modeling, learning of cooperative behavior, case-based reasoning