A Unifying Bayesian Formulation of Measures of Interpretability in
  Human-AI
  Existing approaches for generating human-aware agent behaviors have
considered different measures of interpretability in isolation. Further, these
measures have been studied under differing assumptions, thus precluding the
possibility of designing a single framework that captures these measures under
the same assumptions. In this paper, we present a unifying Bayesian framework
that models a human observer's evolving beliefs about an agent and thereby
define the problem of Generalized Human-Aware Planning. We will show that the
definitions of interpretability measures like explicability, legibility and
predictability from the prior literature fall out as special cases of our
general framework. Through this framework, we also bring a previously ignored
fact to light that the human-robot interactions are in effect open-world
problems, particularly as a result of modeling the human's beliefs over the
agent. Since the human may not only hold beliefs unknown to the agent but may
also form new hypotheses about the agent when presented with novel or
unexpected behaviors.
