A multiagent system (MAS) comprises a set of autonomous agents that interact in a shared environment [9]. For example, a smart tra c system is a MAS that includes autonomous agents like cars, tra c lights, etc. The objectives of such a system include ensuring that each agent reaches its destination timely, and that the number of accidents is minimized. For a MAS to achieve its system-level objectives, the complexity and unpredictability of the agent interactions and of the environment must be taken into account. When engineering such systems, the available knowledge of these dynamics is only partial and incomplete. Therefore, MASs need to be regulated at runtime. Norm enforcement is a prominent mechanism for controlling and coordinating the runtime behavior of the agents in a MAS without over-constraining their autonomy [1,8]. Norm enforcement via sanctions is traditionally contrasted with norm regimentation, which prevents the agents from reaching certain states of a airs. For example, in a smart tra c system, a regimentation strategy could be to close a road to prevent cars from entering the road, while a sanctioning strategy could be to impose sanctions on cars that do enter the road. Existing research has studied the o ine revision of the enforced norms, and proposed logics that support norm change [2, 7], and studied the legal e ects of norm change [6]. In previous work [4] we have proposed a framework for engineering normative MASs that, using runtime data from MAS execution, revises the norms in the MAS to maximize the achievement of the system objectives. In such work, we made the simplistic assumption that norms are regimented and we introduced algorithms for switching among alternative norms. In this paper, we make a step forward toward the engineering of normative multiagent systems and we propose a regulatory mechanism that relies on norms with sanction. In our approach, we automatically revise the sanctions that are employed to enforce the norms. To do so, we rst interpret|through a Bayesian Network|runtime execution data in terms of how well certain norms contribute to the achievement of the system-level objectives in di erent operating contexts. Then, we suggest a revision of the sanctions using two di erent heuristic strategies, called Synergy and Sensitivity. The two strategies leverage the knowledge learned from runtime execution data and the knowledge about the
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Synergy: the new sanction is the closest sanction to the current one that is expected to decrease the probability of violating a norm, in case of positive synergy between norm and system-objectives (i.e., objectives are more likely achieved when the norm is obeyed), or to increase it, in case of negative synergy.
Sensitivity not only determines the direction of the revision{i.e.,
increasing or decreasing the probability of violating the norm{, but also provides an
estimation of the required change in such probability in order to make the norm
e ective for the achievement of the system-objectives. Such estimation is
obtained by performing sensitivity analysis for the Bayesian Network [
An evaluation through a tra c regulation simulation shows that our heuristics outperform uninformed heuristics in terms of how fast they identify an optimal solution (i.e., an optimal sanction for the enforced norm). We perform six experiments, di ering in the distribution of types of agents and in the norm enforced in the MAS. We show that in all experiments our informed heuristics, and especially Sensitivity, provide a signi cant improvement in the number of steps required to identify optimal sanctions (reducing the required number of steps up to 98%), compared to the uninformed strategies.