Norm Identification through Plan Recognition
  Societal rules, as exemplified by norms, aim to provide a degree of
behavioural stability to multi-agent societies. Norms regulate a society using
the deontic concepts of permissions, obligations and prohibitions to specify
what can, must and must not occur in a society. Many implementations of
normative systems assume various combinations of the following assumptions:
that the set of norms is static and defined at design time; that agents joining
a society are instantly informed of the complete set of norms; that the set of
agents within a society does not change; and that all agents are aware of the
existing norms. When any one of these assumptions is dropped, agents need a
mechanism to identify the set of norms currently present within a society, or
risk unwittingly violating the norms. In this paper, we develop a norm
identification mechanism that uses a combination of parsing-based plan
recognition and Hierarchical Task Network (HTN) planning mechanisms, which
operates by analysing the actions performed by other agents. While our basic
mechanism cannot learn in situations where norm violations take place, we
describe an extension which is able to operate in the presence of violations.
