Answer Set Programming Energised! End-to-End Neurosymbolic Reasoning and Learning with ASP and Energy Based Models
Learn to integrate Answer Set Programming (ASP) with energy-based models for end-to-end neurosymbolic reasoning and learning, enabling joint optimization in continuous latent space
- Implement ASP-based declarative semantics to incorporate background knowledge and constraints into your energy-based model
- Use ASP to perform non-monotonic inference and joint optimization in the continuous latent space
- Integrate ASP with energy-based models to enable end-to-end neurosymbolic reasoning and learning
- Apply this methodology to real-world problems, such as knowledge graph completion or natural language processing
- Evaluate the performance of the integrated model using metrics such as accuracy and computational efficiency
Researchers and developers in AI, particularly those working on neurosymbolic reasoning and learning, can benefit from this integration to enhance their models' performance and interpretability
💡 The integration of ASP with energy-based models enables joint optimization in continuous latent space, leading to improved performance and interpretability in neurosymbolic reasoning and learning tasks
Integrate ASP with energy-based models for enhanced neurosymbolic reasoning and learning! #NeurosymbolicAI #ASP #EnergyBasedModels
Key Takeaways
Learn to integrate Answer Set Programming (ASP) with energy-based models for end-to-end neurosymbolic reasoning and learning, enabling joint optimization in continuous latent space
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Abstract:
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, probab
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