Runtime Revision of Norms and Sanctions based on Agent Preferences
To fulfill the overall objectives of a multiagent system, runtime norm enforcement is proposed. Due to the system dynamicity, it is hard to specify norms that, when enforced, fulfill the system-level objectives. We propose a mechanism that automatically revise norms using the monitored system data and the agents' preferences. A Bayesian Network is used to learn the relationship between the obedience/violation of a norm and the achievement of the system objectives.
Mehdi Dastani is a professor in artificial intelligence and the chair of Intelligent Systems group of the department of Information and Computing Sciences at Utrecht University.His research interests concern theories and applications of autonomous systems and multi-agent systems, in particular logical and computational models of social and cognitive phenomena such as emotions, norms, responsibility, decision making, interaction, and perception.