  The Model Reconciliation Problem (MRP) was introduced to address issues in
explainable AI planning. A solution to a MRP is an explanation for the
differences between the models of the human and the planning agent (robot).
Most approaches to solving MRPs assume that the robot, who needs to provide
explanations, knows the human model. This assumption is not always realistic in
several situations (e.g., the human might decide to update her model and the
robot is unaware of the updates).
  In this paper, we propose a dialog-based approach for computing explanations
of MRPs under the assumptions that (i) the robot does not know the human model;
(ii) the human and the robot share the set of predicates of the planning domain
and their exchanges are about action descriptions and fluents' values; (iii)
communication between the parties is perfect; and (iv) the parties are
truthful. A solution of a MRP is computed through a dialog, defined as a
sequence of rounds of exchanges, between the robot and the human. In each
round, the robot sends a potential explanation, called proposal, to the human
who replies with her evaluation of the proposal, called response. We develop
algorithms for computing proposals by the robot and responses by the human and
implement these algorithms in a system that combines imperative means with
answer set programming using the multi-shot feature of clingo.
