Interpretable Models for Understanding Immersive Simulations
  This paper describes methods for comparative evaluation of the
interpretability of models of high dimensional time series data inferred by
unsupervised machine learning algorithms. The time series data used in this
investigation were logs from an immersive simulation like those commonly used
in education and healthcare training. The structures learnt by the models
provide representations of participants' activities in the simulation which are
intended to be meaningful to people's interpretation. To choose the model that
induces the best representation, we designed two interpretability tests, each
of which evaluates the extent to which a model's output aligns with people's
expectations or intuitions of what has occurred in the simulation. We compared
the performance of the models on these interpretability tests to their
performance on statistical information criteria. We show that the models that
optimize interpretability quality differ from those that optimize (statistical)
information theoretic criteria. Furthermore, we found that a model using a
fully Bayesian approach performed well on both the statistical and
human-interpretability measures. The Bayesian approach is a good candidate for
fully automated model selection, i.e., when direct empirical investigations of
interpretability are costly or infeasible.
