  The interactive partially observable Markov decision process (I-POMDP) is a
recently developed framework which extends the POMDP to the multi-agent setting
by including agent models in the state space. This paper argues for formulating
the problem of an agent learning interactively from a human teacher as an
I-POMDP, where the agent \emph{programming} to be learned is captured by random
variables in the agent's state space, all \emph{signals} from the human teacher
are treated as observed random variables, and the human teacher, modeled as a
distinct agent, is explicitly represented in the agent's state space. The main
benefits of this approach are: i. a principled action selection mechanism, ii.
a principled belief update mechanism, iii. support for the most common teacher
\emph{signals}, and iv. the anticipated production of complex beneficial
interactions. The proposed formulation, its benefits, and several open
questions are presented.
