  We describe an extension of the Fanoos XAI system [Bayani et al 2022] which
enables the system to learn the appropriate action to take in order to satisfy
a user's request for description to be made more or less abstract.
Specifically, descriptions of systems under analysis are stored in states, and
in order to make a description more or less abstract, Fanoos selects an
operator from a large library to apply to the state and generate a new
description. Prior work on Fanoos predominately used hand-written methods for
operator-selection; this current work allows Fanoos to leverage experience to
learn the best operator to apply in a particular situation, balancing
exploration and exploitation, leveraging expert insights when available, and
utilizing similarity between the current state and past states. Additionally,
in order to bootstrap the learning process (i.e., like in curriculum learning),
we describe a simulated user which we implemented; this simulation allows
Fanoos to gain general insights that enable reasonable courses of action,
insights which later can be refined by experience with real users, as opposed
to interacting with humans completely from scratch. Code implementing the
methods described in the paper can be found at
https://github/DBay-ani/Operator_Selection_Learning_Extensions_For_Fanoos.
