  As AI technology continues to develop, more and more agents will become
capable of long term autonomy alongside people. Thus, a recent line of research
has studied the problem of teaching autonomous agents the concept of ethics and
human social norms. Most existing work considers the case of an individual
agent attempting to learn a predefined set of rules. In reality, however,
social norms are not always pre-defined and are very difficult to represent
algorithmically. Moreover, the basic idea behind the social norms concept is
ensuring that one's actions do not negatively influence others' utilities,
which is inherently a multiagent concept. Thus, here we investigate a way to
teach agents, as a team, how to act according to human social norms. In this
research, we introduce the STAR framework used to teach an ad hoc team of
agents to act in accordance with human social norms. Using a hybrid team
(agents and people), when taking an action considered to be socially
unacceptable, the agents receive negative feedback from the human teammate(s)
who has(have) an awareness of the team's norms. We view STAR as an important
step towards teaching agents to act more consistently with respect to human
morality.
