Dynamics of Neuronal Models in Online Neuroevolution of Robotic Controllers
Fernando Silva, Luís Miguel Parreira Correia, Anders Lyhne Christensen · Figshare · 2013
Dynamics of Neuronal Models in Online Neuroevolution of Robotic Controllers, by Fernando Silva, Luís Correia, and Anders Lyhne Christensen. To appear in: EPIA 2013. 16th Portuguese Conference on Artificial Intelligence. Springer-Verlag. Abstract: In this paper, we investigate the dynamics of different neuronal models on online neuroevolution of robotic controllers in multirobot systems. We compare the performance and robustness of neural network-based controllers using summing neurons, multiplicative neurons, and a combination of the two. We perform a series of simulation-based experiments in which a group of e-puck-like robots must perform an integrated navigation and obstacle avoidance task in environments of different complexity. We show that multiplicative controllers and hybrid controllers maintain stable performance levels across tasks of different complexity. We show that: (i)~summing controllers evolve diverse behaviours that vary qualitatively during task-execution, and that (ii)~multiplicative controllers lead to less diverse and more static behaviours that are maintained despite environmental changes. Complementary, hybrid controllers exhibit both behavioural characteristics, and display superior generalisation capabilities in simple and complex tasks. Description of the dataset: The dataset contains a number of figures and .gv files that represent artificial neural networks used as evolved robotic controllers. For a given experimental setup (see the paper), each controller evolved is named as "S.R", where S represents the sample/run in which the controller was evolved, and R is the id of the robot. Networks are described in the following manner: - Ri denotes sensor i for robot detection, with i in [1:8] - Wi denotes sensor i for wall/obstacle detection, with i in [1:8] - E denotes the virtual energy level sensor - Hx denotes the hidden neuron H with id x - LW represents the output neuron that controls the robot's left wheel - RW represents the output neuron that controls the robot's right wheel