How communication can improve the performance of multi-agent systems

We analyze a general model of multi-agent communication in which all agents learn to communicate simultaneously to a message board. We show that the communicating multiagent system is equivalent to a Mealy finite state machine whose states are determined by the agents' usage of the learned language. Increasing the language size increases the number of possible states in the Mealy machine, and can improve the performance of the multi-agent system. We introduce the term semantic density to describe the average number of meanings assigned to each word of a language. Using semantic density, a simple rule is presented that provides a pessimistic estimate of the minimum language size that should be used for any multi-agent problem in which the agents communicate simultaneously. Simulations on a version of the predator-prey pursuit problem, a simplified version of problems seen in warfare scenarios, validate these predictions. The communicating predators evolved using a genetic algorithm perform significantly b etter than all previous work on similar preys.