## 6 Conclusions  
A set of robotics tasks were proposed and solved using a swarm of homogeneous simulated robots, controlled by Continuous-Time Recurrent Neural Networks (CTRNN). The neural controllers are optimized using Separable Natural Evolution Strategies (SNES). The faced experiments were the leader selection and the swarm borderline identification tasks. These experiments required, at some extent, the cooperative interaction and communication among robots, that are incapable of solving the problem individually. The objective of the leader selection experiment is to elect, automatically and in a distributed fashion, a single leader of the swarm of robots. Alternatively, the borderline identification is a task in which swarm members have to detect if they are in the borderline or shape of the swarm or not. In both experiments, the simulated static robots have to solve the corresponding task in a distributed manner by interacting locally using a communication system. The semantics of the communication are undefined a priori and it corresponds to the evolution process and fine-tuning of the neural controller the settlement of the communication information that becomes relevant. As a side contribution of this paper, we propose a minimal communication system, highly constrained both in terms of spatial range and number of messages that can be received at each time instant. It provides the robots with both a message and the context information attached to it. We analyzed the emerged communication mechanics of both tasks that result from evolution. The main objective and contribution of this paper is the validation of the hypothesis that, depending on the task to be solved, the evolution guides communication towards very different mechanics and semantics, albeit the neural controller is architecturally the same. We show that evolution in the leader selection experiment leads to an abstract communication, while in the borderline identification task only the environmental context is used. Specifically, the abstract communication that emerged in the former experiment was a signaling process in which leaders notify their leadership and non leaders relay the information so that it is spread around the entire swarm. On the contrary, a purely situated communication, in which only the context information about the orientation from where the message was received seems to be relevant, arises in the latter experiment. Finally, we show that, up to considerable limits, the evolved systems are scalable and outstandingly robust under the defined perturbations. After the evolution phase, the scalability of both experiments was assessed by means of increasing the swarm size and observing the consequent results. Additionally, the robustness of the leader selection task was evaluated by imposing leader fault after 50 consecutive time steps of stable leadership. In the borderline identification experiment, the system robustness was verified by drastically altering the swarm topology at runtime. Although the expected results were successfully achieved, there are some limitations that should be taken into consideration and can be stated as future research lines. Firstly, owing to the fact that the experiments are carried out using simulated robots, a future analysis employing real robots must be accomplished in order to address the simulation-to-reality gap. Moreover, the experiments are performed using a swarm of static robots. Thus, it has not been assessed if the system is able to respond suitably in dynamic environments. Additionally, we show that the same neural controller can be optimized to reach the emergence of diverse communication semantics depending on the task to be solved. However, this validation is only assessed for the leader selection and borderline identification problems. Therefore, a future study can extend this work to more demanding and sophisticated tasks that require a more complex and higher level communication semantics.