On the right combination of altruism and randomness in the motion of homogeneous distributed autonomous agents

We demonstrate the role of randomness and altruism in the motion of artificial agents in a deterministic environment. A swarm of distributed autonomous agents with no possibility of coordination tracks a unique target. The goal is to reach the target as efficiently as possible, i.e. with as few agents moving from their current position as possible. We show in two models how, by adopting features of randomness and altruism in the agent’s motion (which, in our case, translates to yielding to other agents), this objective can be reached. In the first, simplistic representation, agents are dimensionless. The system is formulated as a unidimensional Markov chain, and we show how correctly setting the level of randomness in agents’ movement enables optimization of the swarm total energy expenditure. In the second representation, the agent embodiment raises the question of interference in movement. Again, we show how with no possibility of coordination, and based solely on a partial knowledge of the current system state, it is possible to optimize the swarm movements by dynamically adapting the agent level of randomness in movement. These are but two possible representations of a swarm of simple non-cooperating agents sharing a common target. Yet, they demonstrate how moderating the individual agent attraction to the target by introducing the precise level of randomness in the decision to move or not, helps in breaking ties between agents in their race to the target, and to optimize the overall swarm energy expenditure.