Adaptivity at every layer: a modular approach for evolving societies of learning autonomous systems

Authors:
Willi Richert;Bernd Kleinjohann
Affiliations:
University of Paderborn, Paderborn, Germany;University of Paderborn, Paderborn, Germany
Venue:
Proceedings of the 2008 international workshop on Software engineering for adaptive and self-managing systems
Year:
2008

Citing 11
Cited 0

An Behavior-based Robotics

An Behavior-based Robotics
An approach to the analysis and design of multiagent systems based on interaction frames

Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
The Vision of Autonomic Computing

Computer
Introduction to Machine Learning (Adaptive Computation and Machine Learning)

Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Self-Managed Systems: an Architectural Challenge

FOSE '07 2007 Future of Software Engineering
Towards Robust Layered Learning

ICAS '07 Proceedings of the Third International Conference on Autonomic and Autonomous Systems
Increasing the Autonomy of Mobile Robots by On-line Learning Simultaneously at Different Levels of Abstraction

ICAS '08 Proceedings of the Fourth International Conference on Autonomic and Autonomous Systems
On graphs with unique node labels

GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
ORCA – towards an organic robotic control architecture

IWSOS'06/EuroNGI'06 Proceedings of the First international conference, and Proceedings of the Third international conference on New Trends in Network Architectures and Services conference on Self-Organising Systems
Evolving agent societies through imitation controlled by artificial emotions

ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I

Quantified Score

Hi-index	0.01

Visualization

Abstract

We describe a developmental architecture that enables individual robots to fulfill tasks assigned to the robot society in a robust, decentralized manner. The architecture is meant to show emergent properties according to Organic Computing principles that are positive for the society's robustness and performance. This requires the architecture to feature those adaptation and learning processes that are not only selfishly useful for the individual robot, but also incorporate the robot society's actual needs at all layers.