## 1 Introduction
Another important question when designing communication systems in SR, guided by an evolution process, is whether it is possible or not to solve remarkably different tasks using the same robot controller. Moreover, can evolution lead to the emergence of different communication semantics depending on the task to be solved? In this paper, we address these questions. More precisely, the main motivation of this study is to assess if, using evolutionary computation, it is possible to achieve the emergence of opposed communication semantics (e.g., situated and abstract semantics) depending on the task to be solved and using the same neural controller architecture, with different optimization trajectories. In order to verify this concern, we propose two independent swarm robotics problems that require, at first sight, different communication semantics. The tasks are the election of a single leader of a swarm of static robots and the identification of the shape or borderline of a swarm of static robots. It can be expected beforehand that an abstract communication emerges when solving the leader selection problem. Alternatively, the borderline identification task requires, a priori, context information in order to be fulfilled. We consider that the proposed tasks are remarkably suitable benchmark experiments, in order to validate the previously mention hypothesis of communication emergence, due to the following reasons: (i) their successful completion requires communication, (ii) the required semantics of both problems are expected to be different and (iii) both tasks can be addressed using the same artificial neural network as robot's controller (because the robot sensors and actuators are common to both experiments). Thereafter, the main contributions of this paper are the following:  
* We analyze and successfully verify that the same CTRNN controller model, evolved using separable natural evolution strategies, can lead to the emergence of different kinds of communications, either abstract or situated, depending on the problem to be faced. Specifically, we validate the previous statement for the tasks of leader selection and the borderline identification in a swarm of simulated static robots. * A minimal communication system, at disposal of the robots' neural network, is proposed for solving the experiments and validating the hypotheses. The communication system allows robots to communicate directly, using an a priori meaningless message, or indirectly, by means of the corresponding environmental context information. * Apart from observing the communication semantics that emerge as a result of evolution in the proposed tasks, the resulting solutions are subject to a scalability and robustness analysis. In both cases, the obtained swarm robotics system fulfills both properties up to a considerable limit. SNES algorithm is used to evolve the parameters of the neural controllers. Up to our knowledge, NES algorithms have not been applied to the field of swarm robotics, and, even though an exhaustive comparison should be carried out in the future, they are a noticeable alternative to other more widely explored automatic design methods in SR. The remaining of the document is structured as follows. Section 2 provides a review of the related work. Additionally, Section 3 explains the simulated robots, the designed minimal communication system, the robot's neural controller that maps stimuli to actions and the evolution process employed to optimize the controllers. Thereafter, Section 4 describes the proposed tasks to be solved. The results obtained after evolution are exposed in Section 5. Section 6 concludes the paper. Finally, the source code developed and used for this paper is available at [[https://github.com/r-sendra/SpikeSwarmSim](https://github.com/r-sendra/SpikeSwarmSim)]([https://github.com/r-sendra/SpikeSwarmSim](https://github.com/r-sendra/SpikeSwarmSim)) accessed on 22 Janunary 2021.