Data-Driven Revision of Conditional Norms in Multi-Agent Systems (Extended Abstract)

Conference Paper (2023)
Author(s)

Davide Dell’Anna (TU Delft - Control & Simulation)

Natasha Alechina (Universiteit Utrecht)

Fabiano Dalpiaz (Universiteit Utrecht)

Mehdi Dastani (Universiteit Utrecht)

Brian Logan (Universiteit Utrecht, University of Aberdeen)

Research Group
Control & Simulation
More Info
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Publication Year
2023
Language
English
Research Group
Control & Simulation
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Pages (from-to)
6868-6872
ISBN (electronic)
9781956792034
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Abstract

In multi-agent systems, norm enforcement is a mechanism for steering the behavior of individual agents in order to achieve desired system-level objectives. Due to the dynamics of multi-agent systems, however, it is hard to design norms that guarantee the achievement of the objectives in every operating context. Also, these objectives may change over time, thereby making previously defined norms ineffective. In this paper, we investigate the use of system execution data to automatically synthesise and revise conditional prohibitions with deadlines, a type of norms aimed at preventing agents from exhibiting certain patterns of behaviors. We propose DDNR (Data-Driven Norm Revision), a data-driven approach to norm revision that synthesises revised norms with respect to a data set of traces describing the behavior of the agents in the system. We evaluate DDNR using a state-of-the-art, off-the-shelf urban traffic simulator. The results show that DDNR synthesises revised norms that are significantly more accurate than the original norms in distinguishing adequate and inadequate behaviors for the achievement of the system-level objectives.

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