  In its simplest form the well known consensus problem for a networked family
of autonomous agents is to devise a set of protocols or update rules, one for
each agent, which can enable all of the agents to adjust or tune their
"agreement variable" to the same value by utilizing real-time information
obtained from their "neighbors" within the network. The aim of this paper is to
study the problem of achieving a consensus in the face of limited information
transfer between agents. By this it is meant that instead of each agent
receiving an agreement variable or real-valued state vector from each of its
neighbors, it receives a linear function of each state instead. The specific
problem of interest is formulated and provably correct algorithms are developed
for a number of special cases of the problem.
