Logic-Based Explainable and Incremental Machine Learning

Authors

  • Gopal Gupta The University of Texas at Dallas
  • Huaduo Wang The University of Texas at Dallas
  • Kinjal Basu IBM Research
  • Farahad Shakerin The University of Texas at Dallas
  • Parth Padalkar The University of Texas at Dallas
  • Elmer Salazar The University of Texas at Dallas
  • Sarat Chandra Varanasi The University of Texas at Dallas
  • Sopam Dasgupta The University of Texas at Dallas

DOI:

https://doi.org/10.1609/aaaiss.v2i1.27678

Keywords:

Interpretability, Explainability, Logic-based Methods

Abstract

Mainstream machine learning methods lack interpretability, explainability, incrementality, and data-economy. We propose using logic programming (LP) to rectify these problems. We discuss the FOLD family of rule-based machine learning algorithms that learn models from relational datasets as a set of default rules. These models are competitive with state-of-the-art machine learning systems in terms of accuracy and execution efficiency. We also motivate how logic programming can be useful for theory revision and explanation based learning.
AAAI Fall Symposium 2023 Proceedings Cover

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Published

2024-01-22

How to Cite

Gupta, G., Wang, H., Basu, K., Shakerin, F., Padalkar, P., Salazar, E., … Dasgupta, S. (2024). Logic-Based Explainable and Incremental Machine Learning. Proceedings of the AAAI Symposium Series, 2(1), 230–232. https://doi.org/10.1609/aaaiss.v2i1.27678

Issue

Vol. 2 No. 1: Proceedings of the 2023 AAAI Fall Symposia

Section

Assured and Trustworthy Human-centered AI (ATHAI)