Abstract
To achieve system-level properties of a multiagent system, the behavior of individual agents should be controlled and coordinated. One way to control agents without limiting their autonomy is to enforce norms by means of sanctions. The dynamicity and unpredictability of the agents’ interactions in uncertain environments, however, make it hard for designers to specify norms that will guarantee the achievement of the system-level objectives in every operating context. In this paper, we propose a runtime mechanism for the automated revision of norms by altering their sanctions. We use a Bayesian Network to learn, from system execution data, the relationship between the obedience/violation of the norms and the achievement of the system-level objectives. By combining the knowledge acquired at runtime with an estimation of the preferences of rational agents, we devise heuristic strategies that automatically revise the sanctions of the enforced norms. We evaluate our heuristics using a traffic simulator and we show that our mechanism is able to quickly identify optimal revisions of the initially enforced norms.
Highlights
Multiagent systems (MASs) comprise autonomous agents that interact in a shared environment [57]
If we look at the average values, the strategy that performed less well in the 12 experiments is Naive sensitivity analysis, which, in order to find an optimal configuration among the 256 possible configurations, required an average number of steps between 1 and 52
Despite n-conditional probability tables (CPTs) sensitivity analysis performed, on average, better than the other strategies in the 12 experiments, the results show that using that strategy was mostly advantageous when very few configurations were optimal among all the possible ones
Summary
Multiagent systems (MASs) comprise autonomous agents that interact in a shared environment [57]. To achieve the system-level objectives of a MAS, the behavior of the autonomous agents should be controlled and coordinated [11]. A smart traffic system is a MAS that includes autonomous agents like cars, traffic lights, etc. One way to control the behavior of the agents in a MAS without limiting their autonomy is norm enforcement [1, 47]. Norm enforcement via sanctions is traditionally contrasted with norm regimentation; the latter alternative prevents the agents from reaching certain states of affairs. In a smart traffic system, a regimentation strategy is to close a road to prevent cars from entering that road, while a sanctioning strategy is to impose sanctions on cars that drive through the road
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