Large language model (LLM)-based agents increasingly operate in multi-agent systems (MAS) characterised by strategic interaction. However, little is known about whether, and to what extent, different types of messages affect the outcomes of strategic games. By investigating AI agents based on four popular LLMs, playing...
Alessio Buscemi, Daniele Proverbio, A. Di Stefano et al.· 0 citations
A Boundary Negotiating Artifact is designed as a way to mediate meaning in AIRSes, providing an interface for the different stakeholders to collaborate in AI assessment and positioning it as technical backbone in established AIRS frameworks, structuring the collaborative sense-making of the involved stakeholders.
Idoia Landa-Oregi, Tom Deckenbrunnen, Alessio Buscemi et al.· 0 citations
It is argued that legal uncertainty cannot be fully reduced ex ante and that, within reasonable bounds, it is also necessary for regulatory learning because it creates the space in which boundary negotiation over socio-technical meaning can occur.
Tom Deckenbrunnen, Alessio Buscemi, M. Almada et al.· Proceedings of the AAAI/ACM...· 4 citations
The EU AI Act positions regulation as part of the infrastructure for safe, trustworthy and market-ready innovation. Realising this ambition requires regulatory learning: the evidence generated during implementation must be translated into governance and legal knowledge that supports consistent interpretation, effective...
Alessio Buscemi, Tom Deckenbrunnen, Imane Hmiddou et al.· 0 citations
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