The Complexity of Data-Driven Norm Synthesis and Revision

TL;DR

This paper investigates the computational complexity of creating norms from agent behavior traces in multi-agent systems, revealing that the problem is NP-complete and highlighting challenges in norm synthesis.

Contribution

It formally analyzes the complexity of norm synthesis from behavior traces and proves its NP-completeness, providing insights into the difficulty of automating norm design.

Findings

Norm synthesis from behavior traces is NP-complete.

The complexity result highlights challenges in automating norm creation.

Provides a theoretical foundation for future research in norm engineering.

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.