# A New Algorithm to Automate Inductive Learning of Default Theories

arXiv: https://arxiv.org/abs/1707.02693 · HTML: https://mlsanity.com/paper/1707.02693/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2017.

## Built on by (most cited first, 14 of 14)

- [FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability](https://mlsanity.com/paper/2208.07912/) (2022)
- [FOLD-R++: A Scalable Toolset for Automated Inductive Learning of Default Theories from Mixed Data](https://mlsanity.com/paper/2110.07843/) (2021)
- [FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data](https://mlsanity.com/paper/2202.06913/) (2022)
- [ABA Learning via ASP](https://mlsanity.com/paper/2308.15877/) (2023)
- [Learning Assumption-based Argumentation Frameworks](https://mlsanity.com/paper/2305.15921/) (2023)
- [EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning](https://mlsanity.com/paper/2403.10692/) (2024)
- [Learning Brave Assumption-Based Argumentation Frameworks via ASP](https://mlsanity.com/paper/2408.10126/) (2024)
- [CON-FOLD -- Explainable Machine Learning with Confidence](https://mlsanity.com/paper/2408.07854/) (2024)
- [MC3G: Model Agnostic Causally Constrained Counterfactual Generation](https://mlsanity.com/paper/2508.17221/) (2025)
- [A Clustering and Demotion Based Algorithm for Inductive Learning of Default Theories](https://mlsanity.com/paper/2109.12624/) (2021)
- [FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank](https://mlsanity.com/paper/2206.07295/) (2022)
- [Counterfactual Explanation Generation with s(CASP)](https://mlsanity.com/paper/2310.14497/) (2023)
- [Counterfactual Generation with Answer Set Programming](https://mlsanity.com/paper/2402.04382/) (2024)
- [CFGs: Causality Constrained Counterfactual Explanations using goal-directed ASP](https://mlsanity.com/paper/2405.15956/) (2024)

## Abstract (arXiv)

In inductive learning of a broad concept, an algorithm should be able to distinguish concept examples from exceptions and noisy data. An approach through recursively finding patterns in exceptions turns out to correspond to the problem of learning default theories. Default logic is what humans employ in common-sense reasoning. Therefore, learned default theories are better understood by humans. In this paper, we present new algorithms to learn default theories in the form of non-monotonic logic programs. Experiments reported in this paper show that our algorithms are a significant improvement over traditional approaches based on inductive logic programming.