Probably approximately correct learning of Horn envelopes from queries
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Abstract (arXiv)
We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attribute exploration from formal concept analysis is a procedure that solves this problem, but the number of queries it may ask is exponential in the size of the resulting Horn formula in the worst case. We recall a well-known polynomial-time algorithm for learning Horn formulas with membership and equivalence queries and modify it to obtain a polynomial-time probably approximately correct algorithm for learning the Horn envelope of an arbitrary domain.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Jul 2018.
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Citing papers per year: 2023: 2.
2 papers.
- 2023Towards Ordinal Data Sciencecited by 1
- 2023Learning Horn Envelopes via Queries from Large Language Modelscited by 0
- 2023Towards Ordinal Data Science13 Jul 2023
- 2023Learning Horn Envelopes via Queries from Large Language Models20 May 2023