A Prolog skill enables LLMs to structure placement intent into facts and queries while delegating constraint reasoning to symbolic logic for cloud-edge service placement.
DeepProbLog: Neural Probabilistic Logic Programming
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abstract
We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques can be adapted for the new language. Our experiments demonstrate that DeepProbLog supports both symbolic and subsymbolic representations and inference, 1) program induction, 2) probabilistic (logic) programming, and 3) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.
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UNVERDICTED 2representative citing papers
A framework combining legal ontology, rule extraction, and solver reasoning verifies whether AI explanations for CalFresh eligibility align with statutory constraints.
citing papers explorer
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A Neurosymbolic Prolog Skill for LLM-Driven Service Placement
A Prolog skill enables LLMs to structure placement intent into facts and queries while delegating constraint reasoning to symbolic logic for cloud-edge service placement.
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A Neuro-Symbolic Framework for Accountability in Public-Sector AI
A framework combining legal ontology, rule extraction, and solver reasoning verifies whether AI explanations for CalFresh eligibility align with statutory constraints.