  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.
