Abstract. In this work, we study behavioral specialization in a swarm of autonomous robots. In the studied swarm, a robot working repeatedly on the same type of task improves in task performance due to learning. Robots may exploit this positive effect of learning by selecting with higher probability the tasks on which they have improved their performance. However, even though the exploitation of such performanceimproving effects is clearly a benefit, specialization also entails certain costs. Using a task allocation strategy that allows the robots to behaviorally specialize, we study the trade-off between costs and benefits in simulation experiments. Additionally, we give a perspective on the impact of this trade-off in systems that use specialization.

Abstract. In this work, we study behavioral specialization in a swarm of autonomous robots. In the studied swarm, a robot working repeatedly on the same type of task improves in task performance due to learning. Robots may exploit this positive effect of learning by selecting with higher probability the tasks on which they have improved their performance. However, even though the exploitation of such performanceimproving effects is clearly a benefit, specialization also entails certain costs. Using a task allocation strategy that allows the robots to behaviorally specialize, we study the trade-off between costs and benefits in simulation experiments. Additionally, we give a perspective on the impact of this trade-off in systems that use specialization.

Abstract. In this work, we propose a method for self-organized adaptive task partitioning in a swarm of robots. Task partitioning refers to the decomposition of a task into less complex subtasks, which can then be tackled separately. Task partitioning can be observed in many species of social animals, where it provides several benefits for the group. Selforganized task partitioning in artificial swarm systems is currently not widely studied, although it has clear advantages in large groups. We propose a fully decentralized adaptive method that allows a swarm of robots to autonomously decide whether to partition a task into two sequential subtasks or not. The method is tested on a simulated foraging problem. We study the method's performance in two different environments. In one environment the performance of the system is optimal when the foraging task is partitioned, in the other case when it is not. We show that by employing the method proposed in this paper, a swarm of autonomous robots can reach optimal performance in both environments.