Synthetic ethology and the evolution of cooperative communication
Adaptive Behavior
Communication in reactive multiagent robotic systems
Autonomous Robots
Evolving Beharioral Strategies in Predators and Prey
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Evolving teamwork and coordination with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Scaling Teamwork to Very Large Teams
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
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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 multi-agent 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 {\em 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 better than all previous work on similar preys.