Learning from positive and negative examples: New proof for binary alphabets

Lingg, Jonas; Oliveira, Mateus de Oliveira; Wolf, Petra

doi:10.1016/j.ipl.2023.106427

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Consistency Is Np-hard For Fine Bps1

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2024

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“…See the extended version for an alternative proof via a direct reduction from all-eq-sat, inspired by Lingg et al (2024).…”

Section: Consistency Is Np-hard For Fine Bpsmentioning

confidence: 99%

Learning Broadcast Protocols

Fisman,

Izsak,

Jacobs

2024

AAAI

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The problem of learning a computational model from examples has been receiving growing attention. For the particularly challenging problem of learning models of distributed systems, existing results are restricted to models with a fixed number of interacting processes. In this work we look for the first time (to the best of our knowledge) at the problem of learning a distributed system with an arbitrary number of processes, assuming only that there exists a cutoff, i.e., a number of processes that is sufficient to produce all observable behaviors. Specifically, we consider fine broadcast protocols, these are broadcast protocols (BPs) with a finite cutoff and no hidden states. We provide a learning algorithm that can infer a correct BP from a sample that is consistent with a fine BP, and a minimal equivalent BP if the sample is sufficiently complete. On the negative side we show that (a) characteristic sets of exponential size are unavoidable, (b) the consistency problem for fine BPs is NP hard, and (c) that fine BPs are not polynomially predictable.

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“…See the extended version for an alternative proof via a direct reduction from all-eq-sat, inspired by Lingg et al (2024).…”

Section: Consistency Is Np-hard For Fine Bpsmentioning

confidence: 99%