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Learning Monotone Partitions of Partially-Ordered Domains (Work in Progress)

Abstract : We present an algorithm for learning the boundary between an upward-closed set X and its downward-closed complement. The algorithm selects sampling points for which it submits membership queries x ∈ X. Based on the answers and relying on monotonicity, it constructs an approximation of the boundary. The algorithm generalizes binary search on the continuum from one-dimensional (and linearly-ordered) domains to multi-dimensional (and partially-ordered) ones. Applications include the approximation of Pareto fronts in multi-criteria optimization and parameter synthesis for predicates where the influence of parameters is monotone.
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Contributor : Oded Maler Connect in order to contact the contributor
Submitted on : Tuesday, July 4, 2017 - 6:32:36 PM
Last modification on : Wednesday, November 3, 2021 - 4:52:12 AM
Long-term archiving on: : Friday, December 15, 2017 - 1:30:07 AM


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  • HAL Id : hal-01556243, version 1



Oded Maler. Learning Monotone Partitions of Partially-Ordered Domains (Work in Progress). 2017. ⟨hal-01556243⟩



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