Distributed Mode Computation in Open Multi-Agent Systems

Allowing Multi-Agent Systems (MAS) to compute the mode of the agents initial values (i.e., the value with largest cardinality) represents a highly valuable building block for the development of complex decision-making tasks, as it allows agents to identify the central tendency of data or to implement majority voting processes while considering categorical opinions for which average or median values might not be possible to compute. This is especially challenging in the context of Open Multi-Agent Systems (OMAS), where agents are free to join or leave the network, as in this case the outcome of the mode computation process may vary depending on the current participants to the network. In this paper, we propose a novel OMAS mode computation framework where agents select a value from a finite set of alternatives, and compute the mode via the execution in parallel of a novel average-preserving distributed consensus procedure for each of the different alternatives. We complement the paper with simulation results that numerically demonstrate the effectiveness of the proposed approach.