Answer Set Programming Energised! End-to-End Neurosymbolic Reasoning and Learning with ASP and Energy Based Models
arXiv:2607.08136v1 Announce Type: new Abstract: We present a general neurosymbolic reasoning and learning methodology based on a modular integration of answer set programming with an energy based model substrate. Key contributions are: (1) supporting joint optimisation in the continuous latent space through explicit ASP-based declarative semantics fully incorporating background knowledge, constraints, non-monotonic inference; and (2) advancing recent works at the interface of answer sets, probabilistic logic, and answer set modulo theories by providing a generalised model and practical platfor
This paper represents a significant step in neurosymbolic AI, integrating declarative programming with energy-based models to advance reasoning and learning, building on recent work at this interface.
This development could lead to more robust, explainable, and generalizable AI systems that combine the strengths of symbolic reasoning with the learning capabilities of neural networks.
The ability to jointly optimize continuous latent spaces with explicit ASP-based declarative semantics marks a potential shift towards more integrated and powerful AI architectures.
- · AI researchers
- · Developers of complex AI systems
- · Sectors requiring explainable AI
- · Purely black-box AI approaches
Improved performance and interpretability in AI models for complex tasks.
Accelerated development of AI agents capable of more sophisticated reasoning and learning from limited data.
Potential for AI systems that can independently discover new knowledge and formalize it into symbolic representations.
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Read at arXiv cs.AI