48 citations · 174 across the 27 of their papers we have counts for
9 papers · 1 filter
Causal Laws and Multi-Valued Fluents
Enrico Giunchiglia, Joohyung Lee, Vladimir Lifschitz +1
This paper continues the line of work on representing properties of actions in nonmonotonic formalisms that stresses the distinction between being "true" and being "caused", as in…
On Loop Formulas with Variables
Joohyung Lee, Yunsong Meng
Recently Ferraris, Lee and Lifschitz proposed a new definition of stable models that does not refer to grounding, which applies to the syntax of arbitrary first-order sentences. We…
First-Order Stable Model Semantics with Intensional Functions
Michael Bartholomew, Joohyung Lee
In classical logic, nonBoolean fluents, such as the location of an object, can be naturally described by functions. However, this is not the case in answer set programs, where the…
Elementary Sets for Logic Programs
Martin Gebser, Joohyung Lee, Yuliya Lierler
By introducing the concepts of a loop and a loop formula, Lin and Zhao showed that the answer sets of a nondisjunctive logic program are exactly the models of its Clark's completio…
Safe Formulas in the General Theory of Stable Models
Joohyung Lee, Vladimir Lifschitz, Ravi Palla
Safe first-order formulas generalize the concept of a safe rule, which plays an important role in the design of answer set solvers. We show that any safe sentence is equivalent, in…
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…