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This paper presents the outcomes of an open-floor session held at the 13th International Workshop on Engineering Multi-Agent Systems (EMAS 2025), aimed at co-developing a research roadmap for the EMAS community. Participants collaboratively identified and prioritised challenges in engineering large-scale, adaptive multiagent systems, particularly considering the need to engineer systems that can seamlessly integrate learning and reasoning. Through structured group discussions, four key challenges emerged: explainability in heterogeneous environments, environment modeling, handling dynamic contexts, and communication standardisation. For each of the challenges, participants proposed and ranked potential solutions based on impact and effort. The resulting roadmap highlights concrete research directions toward engineering intelligent, explainable, and interoperable multiagent systems that effectively integrate reasoning and learning in dynamic environments.

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Engineering the Next Generation of Multi-agent Systems: A Community Roadmap from EMAS 2025

  • 会议论文

  • 发表日期：

    2026年

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  • Sebastian Rodriguez,
  • Akhila Bairy,
  • Matteo Baldoni,
  • Patrick Benjamin,
  • Constantin Blessing,
  • Nicolas Brandstetter,
  • Amit K. Chopra,
  • Thomas Clemen,
  • Louise A. Dennis,
  • Ahmad Esmaeili,
  • Lu Feng,
  • Angelo Ferrando,
  • Zahra Ghorrati,
  • Victor Guillet,
  • Önder Gürcan,
  • Soham Hans,
  • James Herber,
  • Viviana Mascardi,
  • Marcel Mauri,
  • Jörg P. Müller,
  • John Thangarajah,
  • Rafał Tyl,
  • Yi Yang

摘要

This paper presents the outcomes of an open-floor session held at the 13th International Workshop on Engineering Multi-Agent Systems (EMAS 2025), aimed at co-developing a research roadmap for the EMAS community. Participants collaboratively identified and prioritised challenges in engineering large-scale, adaptive multiagent systems, particularly considering the need to engineer systems that can seamlessly integrate learning and reasoning. Through structured group discussions, four key challenges emerged: explainability in heterogeneous environments, environment modeling, handling dynamic contexts, and communication standardisation. For each of the challenges, participants proposed and ranked potential solutions based on impact and effort. The resulting roadmap highlights concrete research directions toward engineering intelligent, explainable, and interoperable multiagent systems that effectively integrate reasoning and learning in dynamic environments.