The Black Ninjas and the Sniper: On Robust Population Protocols

Benno Lossin, Philipp Czerner, Javier Esparza, Roland Guttenberg, Tobias Prehn

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Population protocols are a model of distributed computation in which an arbitrary number of indistinguishable finite-state agents interact in pairs to decide some property of their initial configuration. We investigate the behaviour of population protocols under adversarial faults that cause agents to silently crash and no longer interact with other agents. As a starting point, we consider the property “the number of agents exceeds a given threshold t”, represented by the predicate x≥t, and show that the standard protocol for x≥t is very fragile: one single crash in a computation with x:=2t-1 agents can already cause the protocol to answer incorrectly that x≥t does not hold. However, a slightly less known protocol is robust: for any number t≥t of agents, at least t-t+1 crashes must occur for the protocol to answer that the property does not hold. We formally define robustness for arbitrary population protocols, and investigate the question whether every predicate computable by population protocols has a robust protocol. Angluin et al. proved in 2007 that population protocols decide exactly the Presburger predicates, which can be represented as Boolean combinations of threshold predicates of the form ∑i=1nai·xi≥t for a1,...,an,t∈Z and modulo prdicates of the form ∑i=1nai·ximodm≥t for a1,…,an,m,t∈N. We design robust protocols for all threshold and modulo predicates. We also show that, unfortunately, the techniques in the literature that construct a protocol for a Boolean combination of predicates given protocols for the conjuncts do not preserve robustness. So the question remains open.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Science and Business Media Deutschland GmbH
Pages206-233
Number of pages28
DOIs
StatePublished - 2025

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15262 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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