Using Surprise Index for Competency Assessment in Autonomous Decision-Making
arXiv (Cornell University)(2023)
摘要
This paper considers the problem of evaluating an autonomous system's
competency in performing a task, particularly when working in dynamic and
uncertain environments. The inherent opacity of machine learning models, from
the perspective of the user, often described as a `black box', poses a
challenge. To overcome this, we propose using a measure called the Surprise
index, which leverages available measurement data to quantify whether the
dynamic system performs as expected. We show that the surprise index can be
computed in closed form for dynamic systems when observed evidence in a
probabilistic model if the joint distribution for that evidence follows a
multivariate Gaussian marginal distribution. We then apply it to a nonlinear
spacecraft maneuver problem, where actions are chosen by a reinforcement
learning agent and show it can indicate how well the trajectory follows the
required orbit.
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