Runtime revision of sanctions in normative multi-agent systems. Dell'Anna, D., Dastani, M., & Dalpiaz, F. Autonomous Agents and Multi-Agent Systems., 34(2):43, 2020. ![Runtime revision of sanctions in normative multi-agent systems [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper Supplement doi abstract bibtex 1 download 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 systemlevel 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.
@article{DBLP:journals/aamas/DellAnnaDD20,
author = {Davide Dell'Anna and
Mehdi Dastani and
Fabiano Dalpiaz},
title = {Runtime revision of sanctions in normative multi-agent systems},
journal = {Autonomous Agents and Multi-Agent Systems.},
volume = {34},
number = {2},
pages = {43},
year = {2020},
url_Paper = {https://doi.org/10.1007/s10458-020-09465-8},
url_Supplement = {https://zenodo.org/record/3712045#.XwihvSgzaUk},
doi = {10.1007/s10458-020-09465-8},
keywords = {Norm Revision, Norms, MAS, Multi-Agent Systems, Sanctions, Preferences, Data Driven Supervision Of Autonomous Systems},
timestamp = {Thu, 06 Aug 2020 01:00:00 +0200},
biburl = {https://dblp.org/rec/journals/aamas/DellAnnaDD20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
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 systemlevel 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.}
}
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