mlsanityData to 7 Oct 2026

A New Algorithm to Automate Inductive Learning of Default Theories

arXiv 1707.02693 ↗First posted 10 Jul 2017cs.LOcited by 14

Not on the map. Why?

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jul 2017.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 3; 2023: 3; 2024: 5; 2025: 1.

14 papers.

Sort:

  1. 2022FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainabilitycited by 15
  2. 2021FOLD-R++: A Scalable Toolset for Automated Inductive Learning of Default Theories from Mixed Datacited by 10
  3. 2022FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Datacited by 7
  4. 2023ABA Learning via ASPcited by 4
  5. 2023Learning Assumption-based Argumentation Frameworkscited by 3
  6. 2024EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learningcited by 3
  7. 2024Learning Brave Assumption-Based Argumentation Frameworks via ASPcited by 3
  8. 2024CON-FOLD -- Explainable Machine Learning with Confidencecited by 2
  9. 2025MC3G: Model Agnostic Causally Constrained Counterfactual Generationcited by 2
  10. 2021A Clustering and Demotion Based Algorithm for Inductive Learning of Default Theoriescited by 0
  11. 2022FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rankcited by 0
  12. 2023Counterfactual Explanation Generation with s(CASP)cited by 0
2 more
  1. 2024Counterfactual Generation with Answer Set Programmingcited by 0
  2. 2024CFGs: Causality Constrained Counterfactual Explanations using goal-directed ASPcited by 0