Runtime revision of sanctions in normative multi-agent systems

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 u...

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Veröffentlicht in:Autonomous agents and multi-agent systems Jg. 34; H. 2
Hauptverfasser: Dell’Anna, Davide, Dastani, Mehdi, Dalpiaz, Fabiano
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.10.2020
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Bayesian analysis
Computer Science
Computer Systems Organization and Communication Networks
Current Trends in Research on Software Agents and Agent-Based Software Development
Knowledge acquisition
Multiagent systems
Norms
Sanctions
Software Engineering/Programming and Operating Systems
User Interfaces and Human Computer Interaction
Norm revision
Multiagent systems
Norm enforcement
ISSN:1387-2532, 1573-7454
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Zusammenfassung: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.
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ISSN:1387-2532
1573-7454
DOI:10.1007/s10458-020-09465-8