mlsanityData to 14 Oct 2026

Intersymbolic AI: Interlinking Symbolic AI and Subsymbolic AI

arXiv 2406.11563 ↗First posted 17 Jun 2024cs.AIcited by 3

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Abstract (arXiv)

This perspective piece calls for the study of the new field of Intersymbolic AI, by which we mean the combination of symbolic AI, whose building blocks have inherent significance/meaning, with subsymbolic AI, whose entirety creates significance/effect despite the fact that individual building blocks escape meaning. Canonical kinds of symbolic AI are logic, games and planning. Canonical kinds of subsymbolic AI are (un)supervised machine and reinforcement learning. Intersymbolic AI interlinks the worlds of symbolic AI with its compositional symbolic significance and meaning and of subsymbolic AI with its summative significance or effect to enable culminations of insights from both worlds by going between and across symbolic AI insights with subsymbolic AI techniques that are being helped by symbolic AI principles. For example, Intersymbolic AI may start with symbolic AI to understand a dynamic system, continue with subsymbolic AI to learn its control, and end with symbolic AI to safely use the outcome of the learned subsymbolic AI controller in the dynamic system. The way Intersymbolic AI combines both symbolic and subsymbolic AI to increase the effectiveness of AI compared to either kind of AI alone is likened to the way that the combination of both conscious and subconscious thought increases the effectiveness of human thought compared to either kind of thought alone. Some successful contributions to the Intersymbolic AI paradigm are surveyed here but many more are considered possible by advancing Intersymbolic AI.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 23 of its 112 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

23 of 112 references.

References · 23

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11 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Next Steps in LLM-Supported Java Verificationcited by 3
  2. 2025Towards Responsible and Trustworthy Educational Data Mining: Comparing Symbolic, Sub-Symbolic, and Neural-Symbolic AI Methodscited by 3
  3. 2026Protected Cores Are Not Enough: Certifying AI-Proposed Revisions of Temporal Specificationscited by 0

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  2. 2025Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems
  3. 2024Three Pathways to Neurosymbolic Reinforcement Learning with Interpretable Model and Policy Networks
  4. 2025A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives
  5. 2024Interpretable end-to-end Neurosymbolic Reinforcement Learning agents
  6. 2025Mutual Understanding between People and Systems via Neurosymbolic AI and Knowledge Graphs
  7. 2026Neuro-Symbolic Artificial Intelligence: A Task-Directed Survey in the Black-Box Models Era
  8. 2024Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents