Paper Accepted @EUMAS2022

The paper titled Introducing Normative Advisors into MAS by Mostafa Mohajeri Parizi, Thomas van Binsbergen, Giovanni Sileno and Tom van Engers was accepted at The 19th European Conference on Multi-Agent Systems.

Abstract:

This paper introduces a modular architecture for integrating norms in autonomous agents and multi-agent systems. As the the interactions between norms and agents can be complex, this architecture utilizes multiple programmable components to model concepts such as adoption of multiple personal and/or collective possibly conflicting norms, interpretation and qualification between social and normative contexts, possibility of intentionally (non-)compliant behaviors and resolving conflicts between norms and desires (or other norms). The architecture revolves around normative advisors that act as the bridge between intentional agents and the institutional reality. As a technical contribution, a running implementation of the architecture is presented based on the ASC2 (AgentScript) BDI framework and eFLINT norm reasoner.

Giovanni Sileno
Giovanni Sileno
Assistant Professor
L. Thomas van Binsbergen
L. Thomas van Binsbergen
Associate Professor

Thomas van Binsbergen is an Associate Professor in software engineering with a specialisation in the operational semantics of programming languages. Topics of interest include modular language specifications and implementations. Applications areas are automated legal reasoning in multi-domain systems and domain-specific languages for domains such as regulatory and legal, programmable networks, and software language engineering. I am tenured at the University of Amsterdam and situated in the Complex-Cyber Infrastructure research group of the Informatics Institute where I lead the Programming Languages and Verification team.

Tom van Engers
Tom van Engers
Full Professor (FDR)

I conduct research on AI & Law, with a particular focus on normative reasoning. Having a track record in AI & Law research going back to 1983, I have worked both on knowledge-driven as well as data-driven AI approaches.

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