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.

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Published

2024-01-22

Issue

Section

Assured and Trustworthy Human-centered AI (ATHAI)