# On Adversary Robust Consensus protocols through joint-agent interactions

## TL;DR

This paper introduces a broad class of adversary-robust consensus protocols for multi-agent systems, utilizing joint-agent interactions and Petri Net analysis to ensure convergence despite faulty or malicious agents.

## Contribution

It proposes a generalized framework for robust consensus algorithms based on monotone joint-agent interactions and Petri Net modeling, offering new convergence analysis tools.

## Key findings

- Protocols are resilient to malicious agents.
- Petri Net invariants characterize convergence.
- Framework applies to diverse multi-agent systems.

## Abstract

A generalized family of Adversary Robust Consensus protocols is proposed and analyzed. These are distributed algorithms for multi-agents systems seeking to agree on a common value of a shared variable, even in the presence of faulty or malicious agents which are updating their local state according to the protocol rules. In particular, we adopt monotone joint-agent interactions, a very general mechanism for processing locally available information and allowing cross-comparisons between state-values of multiple agents simultaneously. The salient features of the proposed class of algorithms are abstracted as a Petri Net and convergence criteria for the resulting time evolutions formulated by employing structural invariants of the net.