arXiv:2606.14463v1 Announce Type: new Abstract: Neurosymbolic (NeSy) models integrate neural networks and symbolic reasoning for robust and interpretable AI. State-of-the-art NeSy models require that the symbolic component is expressed in a differentiable way, often complicating the use of approximate inference. We propose EM-NeSy which casts probabilistic NeSy learning as an instance of the Expectation-Maximization (EM) algorithm. In the expectation step, we compute the posterior over the neurally predicted symbols conditioned on the label via probabilistic inference. In the maximization step
The continuous drive towards more robust and interpretable AI systems, especially with the limitations of purely differentiable symbolic components, necessitates new approaches like EM-NeSy.
This development addresses a fundamental challenge in neurosymbolic AI, potentially leading to more advanced, reliable, and deployable AI systems that combine the strengths of neural networks and symbolic reasoning.
The explicit casting of probabilistic neurosymbolic learning as an Expectation-Maximization problem simplifies the integration of approximate inference, broadening the scope of symbolic components that can be used.
- · AI researchers
- · Developers of interpretable AI
- · Sectors requiring high AI reliability (e.g., healthcare, finance)
- · Companies investing in neurosymbolic AI
- · Purely black-box neural network approaches
- · Systems heavily reliant on purely differentiable symbolic reasoning
More sophisticated neurosymbolic AI models become feasible, leveraging a wider range of symbolic reasoning techniques.
Improved interpretability and robustness could accelerate AI adoption in sensitive domains where trust and explainability are paramount.
The enhanced capabilities of NeSy models might enable breakthroughs in AGI development by unifying different AI paradigms more effectively.
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Read at arXiv cs.LG