Most social activities are conducted with multiple entities to
improve efficiency. It is difficult to estimate post-learning performance in
advance when multiple entities (e.g., another user, robot, or autonomous
agent) make decisions because they should consider each other’s subsequent
behavior. Personality traits analyzed from responses to the questionnaire
are easy to use for estimating performance but have low estimation
accuracy. Machine learning using behavioral data on a target task
can ...
Most social activities are conducted with multiple entities to
improve efficiency. It is difficult to estimate post-learning performance in
advance when multiple entities (e.g., another user, robot, or autonomous
agent) make decisions because they should consider each other’s subsequent
behavior. Personality traits analyzed from responses to the questionnaire
are easy to use for estimating performance but have low estimation
accuracy. Machine learning using behavioral data on a target task
can provide high accuracy, but such data is not available in advance. To
solve this trade-off between in-advance estimability and estimation accuracy,
we focused on the consistency of how people adjust their behavior
to other collaborators in multiple tasks. We propose utilizing this consistency
to estimate collaborative performance without performing the
target task. We assumed that analyzing behavior adjustment in another
task helps estimate performance of the target task. Our experimental results
indicated that analyzing behavior adjustment in another task was
beneficial to estimate collaborative performance in the target task, compared
with personality-related questionnaire answers or performance of
another task.
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