# A new algorithm to automate inductive learning of default theories*

@article{Shakerin2017ANA, title={A new algorithm to automate inductive learning of default theories*}, author={Farhad Shakerin and Elmer Salazar and Gopal Gupta}, journal={Theory and Practice of Logic Programming}, year={2017}, volume={17}, pages={1010 - 1026} }

Abstract 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…

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## 16 Citations

Cumulative Scoring-Based Induction of Default Theories

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The FOLD 2.0 algorithm is introduced – an enhanced version of the recently developed algorithm called FOLD, which is the first heuristic based, scalable, and noise-resilient ILP system to induce answer set programs.

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A heuristic based algorithm to induce nonmonotonic logic programs that will explain the behavior of XGBoost trained classifiers and a proposed approach is agnostic to the choice of the ILP algorithm.

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- Computer Science, MathematicsILP Up-and-Coming / Short Papers
- 2018

This paper extends previous work on learning stratified answer set programs that have a single stable model to learning arbitrary ones with multiple stable models, capable of inducing non-monotonic logic programs, examples of which includes programs for combinatorial problems such as graph-coloring and N-queens.

White-box Induction From SVM Models: Explainable AI with Logic Programming

- Computer ScienceTheory and Practice of Logic Programming
- 2020

This work focuses on the problem of inducing logic programs that explain models learned by the support vector machine (SVM) algorithm, and develops an algorithm that captures the SVM model’s underlying logic and outperforms other ILP algorithms in terms of the number of induced clauses and classification evaluation metrics.

Whitebox Induction of Default Rules Using High-Utility Itemset Mining

- Computer SciencePADL
- 2020

A fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models and a significant improvement in terms of classification evaluation metrics and training time compared to ALEPH, a state-of-the-art Inductive Logic Programming (ILP) system are suggested.

A Clustering and Demotion Based Algorithm for Inductive Learning of Default Theories

- Computer ScienceArXiv
- 2021

A combination of K-Means clustering and demotion strategy produces significant improvement for datasets with more than one cluster of positive examples, and the resulting induced program is also more concise and therefore easier to understand compared to the FOLD and ALEPH systems.

Induction of Non-Monotonic Rules From Statistical Learning Models Using High-Utility Itemset Mining

- Computer Science, MathematicsArXiv
- 2019

A fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models and a significant improvement in terms of classification evaluation metrics and running time of the training algorithm compared to ALEPH, a state-of-the-art Inductive Logic Programming (ILP) system are suggested.

FOLD-R++: A Toolset for Automated Inductive Learning of Default Theories from Mixed Data

- Computer ScienceArXiv
- 2021

Experiments presented in this paper show that the improved FOLD-R++ algorithm is a significant improvement over the original design and that the s(CASP) system can make predictions in an efficient manner as well.

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- Computer ScienceICLP Workshops
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