PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 12, 20260 citationsOpen Access

Actions Speak Louder Than Words: Evidence-Based Trust Level Evaluation in Multi-Agent Systems

View Full Paper
NFNikolaos FotosKOKoffi Ismael OuattaraDKDimitrios S. Karas

Key Points

  • The research aims to develop a generalized methodology for assessing trust in multi-agent systems across different domains.
  • Proposed a generic methodology for trust level calculation.
  • Applied the methodology in a smart healthcare context.
  • Conducted systematic experiments to evaluate feasibility and effectiveness.
  • Identified challenges in practical trust assessment applications.
  • Demonstrated the effectiveness of the proposed trust assessment methodology.
  • Highlighted key challenges in implementing trust evaluations in practice.
  • Addressed fundamental gaps in existing trust assessment methods.

Abstract

Trust assessment in multi-agent systems (MAS) is critical for ensuring reliable decision-making in dynamic, decentralized environments. However, existing methods for evaluating trust are domain-specific, fragmented, and difficult to generalize. To address this, we propose a generic methodology for trust level calculation that can be instantiated based on domain-specific requirements. We apply this methodology in a smart healthcare use case, where trust is assessed for medical data exchanged between smart ambulances and hospital backends. Through systematic experimentation, we evaluate the feasibility and effectiveness of our approach, identifying key challenges that arise when applying such a trust assessment methodology in practice. These insights allow us to analyze fundamental gaps that must be addressed to further advance the formalization of trust assessment methodologies.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Fotos et al. (2025) studied this question.

synapsesocial.com/papers/69b25b5496eeacc4fcec9f7chttps://doi.org/10.5281/zenodo.18937517
Ask AI
Helpful
Bookmark
Share
View Full Paper