Evaluation of techniques for a learning-driven modeling methodology in multiagent simulation

Authors:
Robert Junges;Franziska Klügl
Affiliations:
Modeling and Simulation Research Center, Örebro University, Sweden;Modeling and Simulation Research Center, Örebro University, Sweden
Venue:
MATES'10 Proceedings of the 8th German conference on Multiagent system technologies
Year:
2010

Citing 11
Cited 4

Automatic programming of behavior-based robots using reinforcement learning

Artificial Intelligence
Technical Note: \cal Q-Learning

Machine Learning
Introduction to artificial life

Introduction to artificial life
Machine Learning

Machine Learning
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Simulation for behavior learning of multi-agent robot

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Classifier fitness based on accuracy

Evolutionary Computation
Learning enabled cooperative agent behavior in an evolutionary and competitive environment

Neural Computing and Applications
Agent Learning Instead of Behavior Implementation for Simulations --- A Case Study Using Classifier Systems

MATES '08 Proceedings of the 6th German conference on Multiagent System Technologies
Performance Comparison of Relational Reinforcement Learning and RBF Neural Networks for Small Mobile Robots

FGCNS '08 Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking Symposia - Volume 04
Learning automata as a basis for multi agent reinforcement learning

LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems

Evolution for modeling: a genetic programming framework for sesam

Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Generating inspiration for agent design by reinforcement learning

Information and Software Technology
How to design agent-based simulation models using agent learning

Proceedings of the Winter Simulation Conference
Behavior Abstraction Robustness in Agent Modeling

WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02

Quantified Score

Hi-index	0.00

Visualization

Abstract

There have been a number of suggestions for methodologies supporting the development of multiagent simulation models. In this contribution we are introducing a learning-driven methodology that exploits learning techniques for generating suggestions for agent behavior models based on a given environmental model. The output must be human-interpretable. We compare different candidates for learning techniques - classifier systems, neural networks and reinforcement learning - concerning their appropriateness for such a modeling methodology.