# Defeasible Reasoning via Datalog^neg

arXiv: https://arxiv.org/abs/2106.10946 · HTML: https://mlsanity.com/paper/2106.10946/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 105 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [The Vadalog System: Datalog-based Reasoning for Knowledge Graphs](https://mlsanity.com/paper/1807.08709/) (2018)
- [Rethinking Defeasible Reasoning: A Scalable Approach](https://mlsanity.com/paper/2001.00406/) (2020)
- [Scaling-Up In-Memory Datalog Processing: Observations and Techniques](https://mlsanity.com/paper/1812.03975/) (2018)
- [Annotated Defeasible Logic](https://mlsanity.com/paper/1707.04734/) (2017)
- [Scaling-Up Reasoning and Advanced Analytics on BigData](https://mlsanity.com/paper/1807.02957/) (2018)
- [Declarative Programming for Microcontrollers -- Datalog on Arduino](https://mlsanity.com/paper/1909.00043/) (2019)
- [Formulog: Datalog for SMT-Based Static Analysis (Extended Version)](https://mlsanity.com/paper/2009.08361/) (2020)
- [On Signings and the Well-Founded Semantics](https://mlsanity.com/paper/2102.06495/) (2021)

## Abstract (arXiv)

We address the problem of compiling defeasible theories to Datalog^neg programs. We prove the correctness of this compilation, for the defeasible logic DL(partial||), but the techniques we use apply to many other defeasible logics. Structural properties of DL(partial||) are identified that support efficient implementation and/or approximation of the conclusions of defeasible theories in the logic, compared with other defeasible logics. We also use previously well-studied structural properties of logic programs to adapt to incomplete Datalog^neg implementations.