# Graph-based Interpretation of Normal Logic Programs

arXiv: https://arxiv.org/abs/2111.13249 · HTML: https://mlsanity.com/paper/2111.13249/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2021.

## Built on by (most cited first, 1 of 1)

- [Inductive Learning for Possibilistic Logic Programs Under Stable Models](https://mlsanity.com/paper/2510.07069/) (2025)

## Abstract (arXiv)

In this paper we present a dependency graph-based method for computing the various semantics of normal logic programs. Our method employs textitconjunction nodes to unambiguously represent the dependency graph of normal logic programs. The dependency graph can be transformed suitably in a semantics preserving manner and re-translated into an equivalent normal logic program. This transformed normal logic program can be augmented with a few rules written in answer set programming (ASP), and the CLINGO system used to compute its answer sets. Depending on how these additional rules are coded in ASP, one can compute models of the original normal logic program under the stable model semantics, the well-founded semantics, or the co-stable model semantics. In each case, justification for each atom in the model is also generated. We report on the implementation of our method as well as its performance evaluation.