Abstract
The behaviour of norm-autonomous agents is determined by their goals and the
norms that are explicitly represented inside their minds. Thus, they require
mechanisms for acquiring and accepting norms, determining when norms are
relevant to their case, and making decisions about norm compliance. Up un-
til now the existing proposals on norm-autonomous agents assume that agents
interact within a deterministic environment that is certainly perceived. In prac-
tice, agents interact by means of sensors and actuators under uncertainty with
non-deterministic and dynamic environments. Therefore, the existing propos-
als are unsuitable or, even, useless to be applied when agents have a physical
presence in some real-world environment. In response to this problem we have
developed the n-BDI architecture. In this paper, we propose a multi -context
graded BDI architecture (called n-BDI) that models norm-autonomous agents
able to deal with uncertainty in dynamic environments. The n-BDI architecture
has been experimentally evaluated and the results are shown in this paper.
