Neuro-Symbolic AI in 2024: A Systematic Review

Abstract Background: The field of Artificial Intelligence has undergone cyclical periods of growth and decline, known as AI summers and winters. Currently, we are in the third AI summer, characterized by significant advancements and commercialization, particularly in the integration of Symbolic AI and Sub-Symbolic AI, leading to the emergence of Neuro-Symbolic AI. Contributions: (1) A definition of Meta-Cognition within Neuro-Symbolic AI. (2) A review of the key themes of the literature post the Neuro-Symbolic research explosion from 2020-2024. (3) Identification of the current gaps in the literature of Neuro-Symbolic AI Objective: This paper provides a systematic literature review of Neuro-Symbolic AI projects within the 2020-24 AI landscape, highlighting key developments, methodologies, and applications. It aims to identify where quality efforts are focused in 2024 and pinpoint existing research gaps in the field. Methods: The review followed the PRISMA methodology, utilizing databases such as IEEE Explore, Google Scholar, arXiv, ACM, and SpringerLink. The inclusion criteria targeted peer-reviewed papers published between 2020 and 2024.

Introduction. The field of Artificial Intelligence (AI) has experienced significant cyclical growth, known as AI summers and winters. At present, we as a community find ourselves in the third AI summer, marked by rapid scientific advances and commercialization, continuing the legacy of previous periods of AI excitement followed by setbacks [1]. A significant product of the third AI summer has been the integration of two prominent fields of AI; Symbolic AI and Sub-Symbolic AI, the fusion of which is known as Neuro-Symbolic AI. There is an ongoing debate about the necessity of Neuro-Symbolic AI [2], opponents arguing that common sense reasoning can be addressed through the use of big data [3] and proponents arguing that “You can’t get to the moon by climbing successively taller trees” [4].

Discussion / Conclusion. The field of Neuro-Symbolic AI has experienced a notable surge in research activity from 2020 onwards, reflecting the growing recognition of the importance of integrating symbolic and sub-symbolic approaches to enhance AI’s reasoning capabilities. The contribution from this systematic literature review is a well-grounded definition of Meta-Cognition within Neuro-Symbolic AI, a review of the key themes of the literature post the Neuro-Symbolic research explosion from 2020-2024 and an identification of the current gaps in the literature of Neuro-Symbolic AI. We found that the majority of the research efforts in between 2020-24 were concentrated in the areas of learning and inference, with a significant portion also dedicated to logic and reasoning, as well as knowledge representation. These areas have seen substantial advancements, with innovative projects and methodologies pushing the boundaries of what AI systems can achieve in terms of understanding, reasoning, and generating human-like responses. However, our review also identifies several critical gaps in the current literature. Despite the substantial progress in learning and inference, there remains a relative sparseness of research focused on explainability and trustworthiness.