H. Iba. Information Sciences, 108 (1-4):
181--205(Juli 1998)
Zusammenfassung
This paper presents the emergence of the cooperative
behavior for communicating agents by means of Genetic
Programming (GP). Our experimental domains are the
pursuit game and the robot navigation task. We conduct
experiments with the evolution of the communicating
agents and show the effectiveness of the emergent
communication in terms of the robustness of generated
GP programs. The performance of GP-based multi-agent
learning is discussed with comparative experiments by
using different breeding strategies, i.e., homogenous
breeding and heterogeneous breeding.
%0 Journal Article
%1 Iba:1998:ISJ
%A Iba, Hitoshi
%D 1998
%J Information Sciences
%K Distributed Multi-agent algorithms, artificial genetic intelligence programming, system,
%N 1-4
%P 181--205
%T Evolutionary learning of communicating agents
%U http://www.sciencedirect.com/science/article/B6V0C-3TKS65B-F/2/ecac160ea272b4818c97d3aab09527d4
%V 108
%X This paper presents the emergence of the cooperative
behavior for communicating agents by means of Genetic
Programming (GP). Our experimental domains are the
pursuit game and the robot navigation task. We conduct
experiments with the evolution of the communicating
agents and show the effectiveness of the emergent
communication in terms of the robustness of generated
GP programs. The performance of GP-based multi-agent
learning is discussed with comparative experiments by
using different breeding strategies, i.e., homogenous
breeding and heterogeneous breeding.
@article{Iba:1998:ISJ,
abstract = {This paper presents the emergence of the cooperative
behavior for communicating agents by means of Genetic
Programming (GP). Our experimental domains are the
pursuit game and the robot navigation task. We conduct
experiments with the evolution of the communicating
agents and show the effectiveness of the emergent
communication in terms of the robustness of generated
GP programs. The performance of GP-based multi-agent
learning is discussed with comparative experiments by
using different breeding strategies, i.e., homogenous
breeding and heterogeneous breeding.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Iba, Hitoshi},
biburl = {https://www.bibsonomy.org/bibtex/241bdcd3dfd04194c0647549e15207e71/brazovayeye},
interhash = {0bba8102fca017dab354983b04bef651},
intrahash = {41bdcd3dfd04194c0647549e15207e71},
issn = {0020-0255},
journal = {Information Sciences},
keywords = {Distributed Multi-agent algorithms, artificial genetic intelligence programming, system,},
month = {July},
notes = {Information Sciences
http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt},
number = {1-4},
pages = {181--205},
timestamp = {2008-06-19T17:42:03.000+0200},
title = {Evolutionary learning of communicating agents},
url = {http://www.sciencedirect.com/science/article/B6V0C-3TKS65B-F/2/ecac160ea272b4818c97d3aab09527d4},
volume = 108,
year = 1998
}