Belle, VaishakVaishakBelleFisher, MichaelMichaelFisherRusso, AlessandraAlessandraRussoKomendantskaya, EkaterinaEkaterinaKomendantskayaNottle, AlistairAlistairNottle2024-06-252024-03-29Autonomous Agents and Multiagent Systems. Best and Visionary Papers, 2024, pp.180-20097830315625490302-9743http://hdl.handle.net/10044/1/112654To get one step closer to “human-like” intelligence, we need systems capable of seamlessly combining the neural learning power of symbolic feature extraction from raw data with sophisticated symbolic inference mechanisms for reasoning about “high-level” concepts. It is important to also incorporate existing prior knowledge about a given problem domain, especially since modern machine learning frameworks are typically data-hungry. Recently the field of neuro-symbolic AI has emerged as a promising paradigm for precisely such an integration. However, coming up with a single, clear, concise definition of this area is not an easy task. There are plenty of variations on this topic, and there is no “one true way” that the community can coalesce around. Recently, a workshop was organized at AAMAS-2023 (London, UK) to discuss how this definition should be broadened to also consider reasoning about agents. This article is a collection of ideas, opinions, and positions from computer scientists who were invited for a panel discussion at the workshop. This collection is not meant to be comprehensive but is rather intended to stimulate further conversation on the field of “Neuro-Symbolic Multi-Agent Systems.”Copyright © 2024 Springer-Verlag. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-56255-6_10Conference Paperhttps://www.dx.doi.org/10.1007/978-3-031-56255-6_10http://dx.doi.org/10.1007/978-3-031-56255-6_102025-03-281611-3349