Combining Multiple Inputs in HyperNEAT Mobile Agent Controller

Authors:
Jan Drchal;Ondrej Kapral;Jan Koutník;Miroslav Šnorek
Affiliations:
Computational Intelligence Group, Department of Computer Science and Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague,;Computational Intelligence Group, Department of Computer Science and Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague,;IDSIA, Manno-Lugano, Switzerland 6928;Computational Intelligence Group, Department of Computer Science and Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague,
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

Citing 10
Cited 1

Evolving neural networks through augmenting topologies

Evolutionary Computation
Training Recurrent Networks by Evolino

Neural Computation
A novel generative encoding for exploiting neural network sensor and output geometry

Proceedings of the 9th annual conference on Genetic and evolutionary computation
Generating large-scale neural networks through discovering geometric regularities

Proceedings of the 9th annual conference on Genetic and evolutionary computation
Generative encoding for multiagent learning

Proceedings of the 10th annual conference on Genetic and evolutionary computation
Hybrid Evolution of Heterogeneous Neural Networks

ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Evolvability of Neuromodulated Learning for Robots

LAB-RS '08 Proceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems
HyperNEAT controlled robots learn how to drive on roads in simulated environment

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
NEAT in HyperNEAT substituted with genetic programming

ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Neuroevolution with analog genetic encoding

PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature

Encouraging reactivity to create robust machines

Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.