Learning Broadcast Protocols

TL;DR

This paper introduces a novel approach to learning distributed systems with an arbitrary number of processes using broadcast protocols, providing algorithms and complexity insights for the problem.

Contribution

It presents the first learning algorithm for distributed systems with an arbitrary number of processes under the cutoff assumption, along with complexity and predictability analyses.

Findings

A learning algorithm for fine broadcast protocols is proposed.

Characteristic sets for learning are exponentially large.

The consistency problem for fine BPs is NP-hard.

Abstract

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.