Swarm Intelligence最新文献

A swarm of robots can collectively select an option among the available alternatives offered by the environment through a process known as collective decision-making. This process is characterised by the fact that once the group makes a decision, it can not be attributed to any of its group members. In swarm robotics, the individual mechanisms for collective decision-making are generally hand-designed and limited to a restricted set of solutions based on the voter or the majority model. In this paper, we demonstrate that it is possible to take an alternative approach in which the individual mechanisms are implemented using artificial neural network controllers automatically synthesised using evolutionary computation techniques. We qualitatively describe the group dynamics underlying the decision process on a collective perceptual discrimination task. We carry out extensive comparative tests that quantitatively evaluate the performance of the most commonly used decision-making mechanisms (voter model and majority model) with the proposed dynamic neural network model under various operating conditions and for swarms that differ in size. The results of our study clearly indicate that the performances of a swarm employing dynamical neural networks as the decision-making mechanism are more robust, more adaptable to a dynamic environment, and more scalable to a larger swarm size than the performances of the swarms employing the voter and the majority model. These results, generated in simulation, are ecologically validated on a swarm of physical e-puck2 robots.

Social insects such as ants and termites communicate via pheromones which allow them to coordinate their activity and solve complex tasks as a swarm, e.g. foraging for food or finding their way back to the nest. This behavior was shaped through evolutionary processes over millions of years. In computational models, self-coordination in swarms has been implemented using probabilistic or pre-defined simple action rules to shape the decision of each agent and the collective behavior. However, manual tuned decision rules may limit the emergent behavior of the swarm. In this work we investigate the emergence of self-coordination and communication in evolved swarms without defining any explicit rule. For this purpose, we evolve a swarm of agents representing an ant colony. We use an evolutionary algorithm to optimize a spiking neural network (SNN) which serves as an artificial brain to control the behavior of each agent. The goal of the evolved colony is to find optimal ways to forage for food and return it to the nest in the shortest amount of time. In the evolutionary phase, the ants are able to learn to collaborate by depositing pheromone near food piles and near the nest to guide other ants. The pheromone usage is not manually encoded into the network; instead, this behavior is established through the optimization procedure. We observe that pheromone-based communication enables the ants to perform better in comparison to colonies where communication via pheromone did not emerge. Furthermore, we assess the foraging performance of the ant colonies by comparing the SNN-based model to a multi-agent rule-based system. Our results show that the SNN-based model can efficiently complete the foraging task in a short amount of time. Our approach illustrates that even in the absence of pre-defined rules, self-coordination via pheromone emerges as a result of the network optimization. This work serves as a proof of concept for the possibility of creating complex applications utilizing SNNs as underlying architectures for multi-agent interactions where communication and self-coordination is desired.