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Found 7 papers, 5 papers with code
NeurASP: Embracing Neural Networks into Answer Set Programming
We present NeurASP, a simple extension of answer set programs by embracing neural networks.
Leveraging Large Language Models to Generate Answer Set Programs
Specifically, we employ an LLM to transform natural language descriptions of logic puzzles into answer set programs.
Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text
It only needs a few examples to guide the LLM's adaptation to a specific task, along with reusable ASP knowledge modules that can be applied to multiple tasks.
Learning to Solve Constraint Satisfaction Problems with Recurrent Transformer
Constraint satisfaction problems (CSPs) are about finding values of variables that satisfy the given constraints.
Injecting Logical Constraints into Neural Networks via Straight-Through Estimators
Injecting discrete logical constraints into neural network learning is one of the main challenges in neuro-symbolic AI.
Extending Answer Set Programs with Neural Networks
While these works aim at extending neural networks with the capability of reasoning, a natural question that we consider is: can we extend answer set programs with neural networks to allow complex and high-level reasoning on neural network outputs?
Translating LPOD and CR-Prolog2 into Standard Answer Set Programs
Logic Programs with Ordered Disjunction (LPOD) is an extension of standard answer set programs to handle preference using the construct of ordered disjunction, and CR-Prolog2 is an extension of standard answer set programs with consistency restoring rules and LPOD-like ordered disjunction.
