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Cited by 25Explainable Artificial Intelligence (XAI)Logic, Reasoning, and KnowledgeAI-based Problem Solving and Planning

A Critical Review of Inductive Logic Programming Techniques for Explainable AI

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Zhang, Zheng, Yilmaz, Levent, Liu, Bo·2024·IEEE Transactions on Neural Networks and Learning Systems
DOI 10.1109/tnnls.2023.3246980
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Abstract

Despite recent advances in modern machine learning algorithms, the opaqueness of their underlying mechanisms continues to be an obstacle in adoption. To instill confidence and trust in artificial intelligence (AI) systems, explainable AI (XAI) has emerged as a response to improve modern machine learning algorithms' explainability. Inductive logic programming (ILP), a subfield of symbolic AI, plays a promising role in generating interpretable explanations because of its intuitive logic-driven framework. ILP effectively leverages abductive reasoning to generate explainable first-order clausal theories from examples and background knowledge. However, several challenges in developing methods inspired by ILP need to be addressed for their successful application in practice. For example, the existing ILP systems often have a vast solution space, and the induced solutions are very sensitive to noises and disturbances. This survey paper summarizes the recent advances in ILP and a discussion of statistical relational learning (SRL) and neural-symbolic algorithms, which offer synergistic views to ILP. Following a critical review of the recent advances, we delineate observed challenges and highlight potential avenues of further ILP-motivated research toward developing self-explanatory AI systems.

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  • ILP can get results rivaling deep learning models while being inherently interpretable and explainable.

    “ILP can get results rivaling deep learning models while being inherently interpretable and explainable [19].”

    From Autonomous Task Completion Based on Goal-directed Answer Set Programming (2025) · cites this paper as [19]

    The Introduction says ILP’s symbolic hypotheses are inspectable by humans and provide transparency, supporting the interpretability and explainability part. The Benchmark ILP problem section reports high accuracy for several ILP systems on selected tasks, but compares ILP systems with one another, not with deep-learning models. The claim that ILP can rival deep learning is therefore not established by these passages. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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  99. Levent Yilmaz is the Alumni Distinguished Professor of Computer Science and Software Engineering at Auburn University with a courtesy appointment in Industrial and Sys- tems Engineering. He holds M.S. and Ph.D. degrees in Computer Science from Virginia Tech. His research interests are in theory and methodology of modeling and simulation, agent-directed simulation, cognitive systems, and model-driven science and engineering for complex adaptive systems. He is the former Editor-in-Chief of Simulation: Transactions of the Society for Mod- eling and Simulation International and the founding organizer and general chair of the Agent-Directed Simulation Conference series.
  100. Bo Liu is a Tenure-Track Assistant Professor with the Department of Computer Science and Software Engineering, Auburn Univer- sity, Auburn, AL, USA. He received the Ph.D. degree from the University of Mas- sachusetts Amherst, Amherst, MA, USA, in 2015. He has over 30 publications on several notable venues. He is the recipient of the UAI'2015 Facebook best student paper award and the Amazon research award in 2018. He is an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (IEEE-TNN), a senior member of IEEE, and a member of AAAI, ACM, and INFORMS.

Authors
Zhang, Z., Yilmaz, L., & Liu, B.
Affiliations
  • Auburn University (US)
  • Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
ORCID
Venue
IEEE Transactions on Neural Networks and Learning Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2024-08
Volume
35
Issue
8
Pages
10220-10236
ISSN
  • 2162-237X (ISSN-L)
  • 2162-2388
DOI
10.1109/tnnls.2023.3246980
OpenAlex
W4362602165
Cited by
25
References
114
Citations by year
2026: 9 · 2025: 9 · 2024: 5 · 2023: 2
FWCI
2.21
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
  • Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
Keywords
inductive logic programming, explainable AI, abductive reasoning, statistical relational learning, neuro-symbolic learning, interpretable explanations, solution space, noise sensitivity

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