A Bayesian Account of Measures of Interpretability in Human-AI
  Interaction
  Existing approaches for the design of interpretable agent behavior consider
different measures of interpretability in isolation. In this paper we posit
that, in the design and deployment of human-aware agents in the real world,
notions of interpretability are just some among many considerations; and the
techniques developed in isolation lack two key properties to be useful when
considered together: they need to be able to 1) deal with their mutually
competing properties; and 2) an open world where the human is not just there to
interpret behavior in one specific form. To this end, we consider three
well-known instances of interpretable behavior studied in existing literature
-- namely, explicability, legibility, and predictability -- and propose a
revised model where all these behaviors can be meaningfully modeled together.
We will highlight interesting consequences of this unified model and motivate,
through results of a user study, why this revision is necessary.
