Runtime Norm Revision Using Bayesian Networks

Abstract

To guarantee the overall intended objectives of a multiagent systems, the behavior of individual agents should be controlled and coordinated. Such coordination can be achieved, without limiting the agents’ autonomy, via runtime norm enforcement. However, due to the dynamicity and uncertainty of the environment, the enforced norms can be ineffective. In this paper, we propose a runtime supervision mechanism that automatically revises norms when their enforcement appears to be ineffective. The decision to revise norms is taken based on a Bayesian Network that gives information about the likelihood of achieving the overall intended system objectives by enforcing the norms. Norms can be revised in three ways: relaxation, strengthening, and alteration. We evaluate the supervision mechanism on an urban smart traffic simulation.