# On the Configuration of More and Less Expressive Logic Programs

arXiv: https://arxiv.org/abs/2203.01024 · HTML: https://mlsanity.com/paper/2203.01024/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 72 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 14 of 14)

- [ParamILS: An Automatic Algorithm Configuration Framework](https://mlsanity.com/paper/1401.3492/) (2014)
- [ASP-Core-2 Input Language Format](https://mlsanity.com/paper/1911.04326/) (2019)
- [Pitfalls and Best Practices in Algorithm Configuration](https://mlsanity.com/paper/1705.06058/) (2017)
- [Model-free, Model-based, and General Intelligence](https://mlsanity.com/paper/1806.02308/) (2018)
- [The Seventh Answer Set Programming Competition: Design and Results](https://mlsanity.com/paper/1904.09134/) (2019)
- [Advancing Lazy-Grounding ASP Solving Techniques -- Restarts, Phase Saving, Heuristics, and More](https://mlsanity.com/paper/2008.03526/) (2020)
- [The Configurable SAT Solver Challenge (CSSC)](https://mlsanity.com/paper/1505.01221/) (2015)
- [claspfolio 2: Advances in Algorithm Selection for Answer Set Programming](https://mlsanity.com/paper/1405.1520/) (2014)
- [Solver Scheduling via Answer Set Programming](https://mlsanity.com/paper/1401.1024/) (2014)
- [A Multi-Engine Approach to Answer Set Programming](https://mlsanity.com/paper/1306.4925/) (2013)
- [Efficiently Coupling the I-DLV Grounder with ASP Solvers](https://mlsanity.com/paper/1810.00041/) (2018)
- [On the Importance of Domain Model Configuration for Automated Planning Engines](https://mlsanity.com/paper/2010.07710/) (2020)
- [Dual-normal Logic Programs - the Forgotten Class](https://mlsanity.com/paper/1507.05388/) (2015)
- [Automated Aggregator -- Rewriting with the Counting Aggregate](https://mlsanity.com/paper/2009.10240/) (2020)

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

- [Reformulation Techniques for Automated Planning: A Systematic Review](https://mlsanity.com/paper/2301.10079/) (2023)

## Abstract (arXiv)

The decoupling between the representation of a certain problem, i.e., its knowledge model, and the reasoning side is one of main strong points of model-based Artificial Intelligence (AI). This allows, e.g. to focus on improving the reasoning side by having advantages on the whole solving process. Further, it is also well-known that many solvers are very sensitive to even syntactic changes in the input. In this paper, we focus on improving the reasoning side by taking advantages of such sensitivity. We consider two well-known model-based AI methodologies, SAT and ASP, define a number of syntactic features that may characterise their inputs, and use automated configuration tools to reformulate the input formula or program. Results of a wide experimental analysis involving SAT and ASP domains, taken from respective competitions, show the different advantages that can be obtained by using input reformulation and configuration. Under consideration in Theory and Practice of Logic Programming (TPLP).