Neuro-symbolic AI + agent systems: a first reflection on trends, opportunities and challenges
Author(s)
Belle, V
Fisher, M
Russo, A
Komendantskaya, E
Nottle, A
Type
Conference Paper
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.”
Date Issued
2024-03-29
Date Acceptance
2023-05-29
ISBN
9783031562549
ISSN
0302-9743
Publisher
Springer Nature Switzerland
Start Page
180
End Page
200
Journal / Book Title
Autonomous Agents and Multiagent Systems. Best and Visionary Papers
Copyright Statement
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_10
Identifier
http://dx.doi.org/10.1007/978-3-031-56255-6_10
Source
AAMAS 2023 Workshops
Publication Status
Published
Start Date
2023-05-29
Finish Date
2023-06-02
Coverage Spatial
London, UK
Rights Embargo Date
2025-03-28
Date Publish Online
2024-03-30