Composing Biases by Using CP to Decompose Minimal Functional Dependencies for Acquiring Complex Formulae
DOI:
https://doi.org/10.1609/aaai.v38i8.28641Keywords:
CSO: Constraint Learning and AcquisitionAbstract
Given a table with a minimal set of input columns that functionally determines an output column, we introduce a method that tries to gradually decompose the corresponding minimal functional dependency (mfd) to acquire a formula expressing the output column in terms of the input columns. A first key element of the method is to create sub-problems that are easier to solve than the original formula acquisition problem, either because it learns formulae with fewer inputs parameters, or as it focuses on formulae of a particular class, such as Boolean formulae; as a result, the acquired formulae can mix different learning biases such as polynomials, conditionals or Boolean expressions. A second key feature of the method is that it can be applied recursively to find formulae that combine polynomial, conditional or Boolean sub-terms in a nested manner. The method was tested on data for eight families of combinatorial objects; new conjectures were found that were previously unattainable. The method often creates conjectures that combine several formulae into one with a limited number of automatically found Boolean terms.Downloads
Published
2024-03-24
How to Cite
Gindullin, R., Beldiceanu, N., Cheukam-Ngouonou, J., Douence, R., & Quimper, C.-G. (2024). Composing Biases by Using CP to Decompose Minimal Functional Dependencies for Acquiring Complex Formulae. Proceedings of the AAAI Conference on Artificial Intelligence, 38(8), 8030-8037. https://doi.org/10.1609/aaai.v38i8.28641
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Section
AAAI Technical Track on Constraint Satisfaction and Optimization