Learning Broadcast Protocols

Dana Fisman, Noa Izsak, Swen Jacobs

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageAmerican English
Title of host publicationTechnical Tracks 14
EditorsMichael Wooldridge, Jennifer Dy, Sriraam Natarajan
Number of pages8
ISBN (Electronic)1577358872, 9781577358879
StatePublished - 25 Mar 2024
Event38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada
Duration: 20 Feb 202427 Feb 2024

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence


Conference38th AAAI Conference on Artificial Intelligence, AAAI 2024

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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