Neuro-symbolic AI + agent systems: a first reflection on trends, opportunities and challenges

Belle, Vaishak, Fisher, Michael, Russo, Alessandra, Komendantskaya, Ekaterina and Nottle, Alistair (2024) Neuro-symbolic AI + agent systems: a first reflection on trends, opportunities and challenges. Amigoni, Francesco and Sinha, Arunesh (eds.) In Autonomous Agents and Multiagent Systems. Best and Visionary Papers - AAMAS 2023 Workshops, Revised Selected Papers. vol. 14456 LNAI, Springer Cham. pp. 180-200 . (doi:10.1007/978-3-031-56255-6_10).

Abstract

To 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.”

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Contributors

Author: Ekaterina Komendantskaya

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