4 citations · 10 across the 8 of their papers we have counts for
9 papers
LPMLN, Weak Constraints, and P-log
Joohyung Lee, Zhun Yang
LPMLN is a recently introduced formalism that extends answer set programs by adopting the log-linear weight scheme of Markov Logic. This paper investigates the relationships betwee…
Think before You Simulate: Symbolic Reasoning to Orchestrate Neural Computation for Counterfactual Question Answering
Adam Ishay, Zhun Yang, Joohyung Lee +2
Causal and temporal reasoning about video dynamics is a challenging problem. While neuro-symbolic models that combine symbolic reasoning with neural-based perception and prediction…
NeurASP: Embracing Neural Networks into Answer Set Programming
Zhun Yang, Adam Ishay, Joohyung Lee
We present NeurASP, a simple extension of answer set programs by embracing neural networks. By treating the neural network output as the probability distribution over atomic facts…
Leveraging Large Language Models to Generate Answer Set Programs
Adam Ishay, Zhun Yang, Joohyung Lee
Large language models (LLMs), such as GPT-3 and GPT-4, have demonstrated exceptional performance in various natural language processing tasks and have shown the ability to solve ce…
Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text
Zhun Yang, Adam Ishay, Joohyung Lee
While large language models (LLMs), such as GPT-3, appear to be robust and general, their reasoning ability is not at a level to compete with the best models trained for specific n…
Learning to Solve Constraint Satisfaction Problems with Recurrent Transformer
Zhun Yang, Adam Ishay, Joohyung Lee
Constraint satisfaction problems (CSPs) are about finding values of variables that satisfy the given constraints. We show that Transformer extended with recurrence is a viable appr…
Injecting Logical Constraints into Neural Networks via Straight-Through Estimators
Zhun Yang, Joohyung Lee, Chiyoun Park
Injecting discrete logical constraints into neural network learning is one of the main challenges in neuro-symbolic AI. We find that a straight-through-estimator, a method introduc…
Extending Answer Set Programs with Neural Networks
Zhun Yang
The integration of low-level perception with high-level reasoning is one of the oldest problems in Artificial Intelligence. Recently, several proposals were made to implement the r…
Translating LPOD and CR-Prolog2 into Standard Answer Set Programs
Joohyung Lee, Zhun Yang
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…