  The development of artificial agents able to learn through dialog without
domain restrictions has the potential to allow machines to learn how to perform
tasks in a similar manner to humans and change how we relate to them. However,
research in this area is practically nonexistent. In this paper, we identify
the modifications required for a dialog system to be able to learn from the
dialog and propose generic approaches that can be used to implement those
modifications. More specifically, we discuss how knowledge can be extracted
from the dialog, used to update the agent's semantic network, and grounded in
action and observation. This way, we hope to raise awareness for this subject,
so that it can become a focus of research in the future.
