@article { tulli20,
	abstract = {The research presented herein addresses the topic of explainability
in autonomous pedagogical agents. We will be investigating
possible ways to explain the decision-making process
of such pedagogical agents (which can be embodied as
robots) with a focus on the effect of these explanations in
concrete learning scenarios for children. The hypothesis is
that the agents’ explanations about their decision making will
support mutual modeling and a better understanding of the
learning tasks and how learners perceive them. The objective
is to develop a computational model that will allow agents to
express internal states and actions and adapt to human's expectations
of cooperative behavior accordingly. In addition,
we would like to provide a comprehensive taxonomy of both
the desiderata and methods in the explainable AI research applied
to children’s learning scenarios.},
	journal = {/},
	keywords = {Social Robotic Companions;Miscellaneous;},
	month = {February},
	pages = {Poster},
	publisher = {AAAI/SIGAI 2020 Doctoral Consortium},
	title = {Explainability in Autonomous Pedagogical Agents},
	year = {2020},
	author = {Silvia Tulli}
}