Combining Multiple Inputs in HyperNEAT Mobile Agent Controller

In this paper we present neuro-evolution of neural network controllers for mobile agents in a simulated environment. The controller is obtained through evolution of hypercube encoded weights of recurrent neural networks (HyperNEAT). The simulated agent’s goal is to find a target in a shortest time interval. The generated neural network processes three different inputs – surface quality, obstacles and distance to the target. A behavior emerged in agents features ability of driving on roads, obstacle avoidance and provides an efficient way of the target search.

• Type: Conference
• Year: 2009
• Where: ICANN
• Authors: Jan Drchal, Ondrej Kapral, Jan Koutník, Miroslav Snorek
• Keywords: Artificial Intelligence, ICANN 2009, Neural Network, Neural Network Controllers, Recurrent Neural Networks