Wireless Sensor Networks (WSNs) are consist of independent distributed sensors with storing, processing, sensing and communication capabilities to monitor physical or environmental conditions. There are number of challenges in WSNs because of limitation of battery power, communications, computation and storage space. In the recent years, computational intelligence approaches such as evolutionar...

in this paper, an intelligent controller is applied to control omni-directional robots motion. first, the dynamics of the three wheel robots, as a nonlinear plant with considerable uncertainties, is identified using an efficient algorithm of training, named lolimot. then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. this emotional lea...

In this thesis, we study distributed reinforcement learning in the context of automating the design of decentralized control for groups of cooperating, coupled robots. Specifically, we develop a framework and algorithms for automatically generating distributed controllers for self-reconfiguring modular robots using reinforcement learning. The promise of self-reconfiguring modular robots is that...

Abstract Several applications in the scientific simulation of physical systems can be formulated as control/optimization problems. The computational models for such generally contain hyperparameters, which control solution fidelity and expense. tuning these parameters is non-trivial general approach to manually ‘spot-check’ good combinations. This because optimal hyperparameter configuration se...

Complex learned behaviors must involve the integrated action of distributed brain circuits. While the contributions of individual regions to learning have been extensively investigated, much less is known about how distributed brain networks orchestrate their activity over the course of learning. To address this gap, we used fMRI combined with tools from dynamic network neuroscience to obtain t...

Reinforcement learning has been extensively studied and applied for generating cooperative behaviours in multi-robot systems. However, traditional reinforcement learning algorithms assume discrete state and action spaces with finite number of elements. This limits the learning to discrete behaviours and cannot be applied to most real multi-robot systems that inherently require appropriate combi...