Cumulative Scoring-Based Induction of Default Theories

Authors Farhad Shakerin , Gopal Gupta

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Farhad Shakerin
  • The University of Texas at Dallas, Texas, USA
Gopal Gupta
  • The University of Texas at Dallas, Texas, USA

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Farhad Shakerin and Gopal Gupta. Cumulative Scoring-Based Induction of Default Theories. In Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018). Open Access Series in Informatics (OASIcs), Volume 64, pp. 2:1-2:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


Significant research has been conducted in recent years to extend Inductive Logic Programming (ILP) methods to induce a more expressive class of logic programs such as answer set programs. The methods proposed perform an exhaustive search for the correct hypothesis. Thus, they are sound but not scalable to real-life datasets. Lack of scalability and inability to deal with noisy data in real-life datasets restricts their applicability. In contrast, top-down ILP algorithms such as FOIL, can easily guide the search using heuristics and tolerate noise. They also scale up very well, due to the greedy nature of search for best hypothesis. However, in some cases despite having ample positive and negative examples, heuristics fail to direct the search in the correct direction. In this paper, we introduce the FOLD 2.0 algorithm - an enhanced version of our recently developed algorithm called FOLD. Our original FOLD algorithm automates the inductive learning of default theories. The enhancements presented here preserve the greedy nature of hypothesis search during clause specialization. These enhancements also avoid being stuck in local optima - a major pitfall of FOIL-like algorithms. Experiments that we report in this paper, suggest a significant improvement in terms of accuracy and expressiveness of the class of induced hypotheses. To the best of our knowledge, our FOLD 2.0 algorithm is the first heuristic based, scalable, and noise-resilient ILP system to induce answer set programs.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Inductive logic learning
  • Inductive Logic Programming
  • Negation As Failure
  • Answer Set Programming
  • Default reasoning
  • Machine learning


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