The Complexity of Data-Driven Norm Synthesis and Revision

Davide Dell'Anna; Natasha Alechina; Brian Logan; Maarten Löffler; Fabiano Dalpiaz; Mehdi Dastani

doi:10.1007/978-3-031-20845-4_3

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arXiv · 2112.02626

The Complexity of Data-Driven Norm Synthesis and Revision

Davide Dell'Anna·Natasha Alechina·Brian Logan·Maarten Löffler·Fabiano Dalpiaz·Mehdi Dastani

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Abstract

Norms have been widely proposed as a way of coordinating and controlling the activities of agents in a multi-agent system (MAS). A norm specifies the behaviour an agent should follow in order to achieve the objective of the MAS. However, designing norms to achieve a particular system objective can be difficult, particularly when there is no direct link between the language in which the system objective is stated and the language in which the norms can be expressed. In this paper, we consider the problem of synthesising a norm from traces of agent behaviour, where each trace is labelled with whether the behaviour satisfies the system objective. We show that the norm synthesis problem is NP-complete.

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Davide Dell'Anna, Natasha Alechina, Brian Logan, Maarten Löffler, Fabiano Dalpiaz, Mehdi Dastani. 2021-12-05. The Complexity of Data-Driven Norm Synthesis and Revision. https://doi.org/10.1007/978-3-031-20845-4_3

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