Achieving Connectivity Between Wide Areas Through Self-Organising Robot Swarm Using Embodied Evolution

Abruptions to the communication infrastructure happens occasionally, where manual dedicated personnel will go out to fix the interruptions, restoring communication abilities. However, sometimes this can be dangerous to the personnel carrying out the task, which can be the case in war situations, environmental disasters like earthquakes or toxic spills or in the occurrence of fire. Therefore, human casualties can be minimised if autonomous robots are deployed that can achieve the same outcome: to establish a communication link between two previously distant but connected sites. In this paper we investigate the deployment of mobile ad hoc robots which relay traffic between them. In order to get the robots to locate themselves appropriately, we take inspiration from self-organisation and emergence in artificial life, where a common overall goal may be achieved if the correct local rules on the agents in system are invoked. We integrate the aspect of connectivity between two sites into the multirobot simulation platform known as JBotEvolver. The robot swarm is composed of Thymio II robots. In addition, we compare three heuristics, of which one uses neuroevolution (evolution of neural networks) to show how self-organisation and embodied evolution can be used within the integration. Our use of embodiment in robotic controllers shows promising results and provide solid knowledge and guidelines for further investigations.

Deep Reservoir Computing Using Cellular Automata

Recurrent Neural Networks (RNNs) have been a prominent concept within artificial intelligence. They are inspired by Biological Neural Networks (BNNs) and provide an intuitive and abstract representation of how BNNs work. Derived from the more generic Artificial Neural Networks (ANNs), the recurrent ones are meant to be used for temporal tasks, such as speech recognition, because they are capable of memorizing historic input. However, such networks are very time consuming to train as a result of their inherent nature. Recently, Echo State Networks and Liquid State Machines have been proposed as possible RNN alternatives, under the name of Reservoir Computing (RC). RCs are far more easy to train. In this paper, Cellular Automata are used as reservoir, and are tested on the 5-bit memory task (a well known benchmark within the RC community). The work herein provides a method of mapping binary inputs from the task onto the automata, and a recurrent architecture for handling the sequential aspects of it. Furthermore, a layered (deep) reservoir architecture is proposed. Performances are compared towards earlier work, in addition to its single-layer version. Results show that the single CA reservoir system yields similar results to state-of-the-art work. The system comprised of two layered reservoirs do show a noticeable improvement compared to a single CA reservoir. This indicates potential for further research and provides valuable insight on how to design CA reservoir systems.

Reservoir Computing Using Non-Uniform Binary Cellular Automata

The Reservoir Computing (RC) paradigm utilizes a dynamical system, i.e., a reservoir, and a linear classifier, i.e., a read-out layer, to process data from sequential classification tasks. In this paper the usage of Cellular Automata (CA) as a reservoir is investigated. The use of CA in RC has been showing promising results. In this paper, selected state-of-the-art experiments are reproduced. It is shown that some CA-rules perform better than others, and the reservoir performance is improved by increasing the size of the CA reservoir itself. In addition, the usage of parallel loosely coupled CA-reservoirs, where each reservoir has a different CA-rule, is investigated. The experiments performed on quasi-uniform CA reservoir provide valuable insights in CA reservoir design. The results herein show that some rules do not work well together, while other combinations work remarkably well. This suggests that non-uniform CA could represent a powerful tool for novel CA reservoir implementations.