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Murray, Cody D.; Williams, Ryan

doi:10.4086/toc.2017.v013a004

Cited by 34 publications

(5 citation statements)

References 21 publications

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“…FT-MOEA uses a multi-objective cost function, which outperforms the one-dimensional cost function in FT-EA. Since FTs encode Boolean functions, FT inference is closely related to synthesis of Boolean circuits with a minimal number of gates [9,19]. Manual simplification of Boolean functions in the context of FT inference is considered in [13].…”

Section: Relatedmentioning

confidence: 99%

Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization

Jimenez-Roa

Volk

Stoelinga

2022

Lecture Notes in Computer Science

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We present SymLearn, a method to automatically infer fault tree (FT) models from data. SymLearn takes as input failure data of the system components and exploits evolutionary algorithms to learn a compact FT matching the input data. SymLearn achieves scalability by leveraging two common phenomena in FTs: (i) We automatically identify symmetries in the failure data set, learning symmetric FT parts only once. (ii) We partition the input data into independent modules, subdividing the inference problem into smaller parts.We validate our approach via case studies, including several truss systems, which are symmetric structures commonly found in infrastructures, such as bridges. Our experiments show that, in most cases, the exploitation of modules and symmetries accelerates the FT inference from hours to under three minutes. This research has been partially funded by NWO under the grant PrimaVera number NWA.1160.18.238 and by the ERC Consolidator grant CAESAR number 864075.

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Section: Relatedmentioning

confidence: 99%

Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization

Jimenez-Roa

Volk

Stoelinga

2022

Lecture Notes in Computer Science

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“…It is a major open question to understand the hardness of MCSP -the problem is clearly in NP, but not known to be in P, and determining if MCSP is NP-hard is a major open question. The "simple" gadget reductions that suffice for textbook NP-hardness results cannot be used to show NP-hardness of MCSP (Murray and Williams, 2017). However, there are efficient randomized Turing reductions from factoring, graph isomorphism, and any problem with a Statistical Zero-Knowledge proof system to MCSP (Allender et al, 2006;Allender and Das, 2017).…”

Section: Related Workmentioning

confidence: 99%

Learning with distributional inverters

Binnendyk¹,

Carmosino²,

Kolokolova³

et al. 2021

Preprint

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We generalize the "indirect learning" technique of Furst et al. (1991) to reduce from learning a concept class over a samplable distribution µ to learning the same concept class over the uniform distribution. The reduction succeeds when the sampler for µ is both contained in the target concept class and efficiently invertible in the sense of Impagliazzo and Luby (1989). We give two applications.• We show that AC 0 [q] is learnable over any succinctly-described product distribution. AC 0 [q] is the class of constant-depth Boolean circuits of polynomial size with AND, OR, NOT, and counting modulo q gates of unbounded fanins. Our algorithm runs in randomized quasi-polynomial time and uses membership queries.• If there is a strongly useful natural property in the sense of Razborov and Rudich (1997) -an efficient algorithm that can distinguish between random strings and strings of non-trivial circuit complexitythen general polynomial-sized Boolean circuits are learnable over any efficiently samplable distribution in randomized polynomial time, given membership queries to the target funuction.

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“…Here is what I wrote at that time: [30] describing various consequences if MCSP were complete under a certain restricted type of ≤ P m reduction). It also fails to adequately give credit to all of the papers that have contributed to this line of work, sincefor example -some of the important contributions of [35] have subsequently been slightly improved [7,25]. But one thing should jump out at the reader from Table 1: All of the conditions listed in Column 3 (with the exception of "FALSE") are widely believed to be true, although they all seem to be far beyond the reach of current proof techniques.…”

Section: Completeness Hardness Reducibilitymentioning

confidence: 99%