Emergent Intelligence of Networked Agents

257 Pages·2007·17.4 MB·English

by  Masaru Aoyagi

AkiraNamatame,SatoshiKuriharaandHideyukiNakashima(Eds.)
EmergentIntelligenceofNetworkedAgents
StudiesinComputationalIntelligence,Volume56
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Akira Namatame
Satoshi Kurihara
Hideyuki Nakashima
(Eds.)
Emergent Intelligence
of Networked Agents
With 105 Figures and 28 Tables
AkiraNamatame SatoshiKurihara
DepartmentofComputerScience GraduateSchoolof
NationalDefenseAcademy InformationScienceandTechnology
1-10-20,Hashirimizu OsakaUniversity
Yokosuka,239-8686 8-1Mihogaoka
Japan Ibaraki,Osaka,567-0047
E-mail:[email protected] Japan
E-mail:[email protected]
HideyukiNakashima
FutureUniversity-Hakodate
Generalco-chairofAAMAS-06
116-2Kamedanakano-choHakodate
Hokkaido041-8655
Japan
E-mail:[email protected]
LibraryofCongressControlNumber:2007921689
ISSNprintedition:1860-949X
ISSNelectronicedition:1860-9503
ISBN-10 3-540-71073-6SpringerBerlinHeidelbergNewYork
ISBN-13 978-3-540-71073-8SpringerBerlinHeidelbergNewYork
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Preface
Recently, the study of intelligence emerged from interactions among many
agentshasbeenpopular.Inthisstudyitisrecognizedthatanetworkstructure
of the agents plays an important role. The current state-of-the art in agent-
based modeling tends to be a mass of agents that have a series of states
that they can express as a result of the network structure in which they are
embedded.Agentinteractionsofallkindsareusuallystructuredwithcomplex
networks. Research on complex networks focuses on scale-freeness of various
kind of networks.
Computational modeling of dynamic agent interactions on richly struc-
turednetworksisimportantforunderstandingthesometimescounter-intuitive
dynamics of such loosely coupled systems of interactions. Yet our tools to
model, understand, and predict dynamic agent interactions and their beha-
vior on complex networks have lagged far behind. Even recent progress in
network modeling has not yet offered us any capability to model dynamic
processes among agents who interact at all scales on such as small-world and
scale-free networks. Generally the high-dimensional, non-linear nature of the
resulting network-centric multi-agent systems makes them difficult or impos-
sible to analyze using traditional methods. Agents follow local rules under
complexnetworkconstraints.Theideaofcombiningmulti-agentsystemsand
complex networks is also particularly rich and fresh to foster the research on
the study of very large-scale multi-agent systems.
Weintendtoturnthisintoanengineeringmethodologytodesigncomplex
agent networks. Multi-agent network dynamics involves the study of many
agents,constituentcomponentsgenerallyactiveoneswithasimplestructures
andwhosebehaviorisassumedtofollowlocalrules,andtheirinteractionson
complex network. A basic methodology is to specify how the agents interact,
and then observe emergent intelligence that occur at the collective level in
order to discover basic principles and key mechanisms for understanding and
shaping the resulting intelligent behavior on network dynamics.
The volume contains refereed papers addressing various important topics
that aims at the investigation of emergent intelligence on networked agents.
vi Preface
Especiallymostpapershighlightonthetopicssuch“networkformationamong
agents”,“influence of network structures on agents”,“network-based collec-
tive phenomena and emergent intelligence on networked agents”.
The selected papers of this volume were presented at the Workshop on
Emergent Intelligence of Networked Agents (WEIN 06) at the Fifth Inter-
national Joint Conference on Autonomous Agents and Multi-agent Systems
(AAMAS2006),whichwasheldatFutureUniversity,Hakodate,Japan,from
May 8 to12, 2006. WEIN 06 is concerned with emergence of intelligent beha-
viors over networked agents and fostering the formation of an active multi-
disciplinary community on multi-agent systems and complex networks. We
especiallyintendedtoincreasetheawarenessofresearchersinthesetwofields
sharing the common view on combining agent-based modeling and complex
networks in order to develop insight and foster predictive methodologies in
studying emergent intelligence on of networked agents. From the broad spec-
trum of activities, leading experts presented important paper and numerous
practicalproblemsappearthroughoutthisbook.Weinvitedhighqualitycon-
tributions on a wide variety of topics relevant to the wide research areas of
multi-agent network dynamics. We especially covered in-depth of important
areas including: Adaptation and evolution in complex networks, Economic
agents and complex networks, Emergence in complex networks, Emergent in-
telligence in multi-agent systems, Collective intelligence, Learning and evolu-
tioninmulti-agentsystems,Webdynamicsascomplexnetworks,Multi-agent
based supply networks, Network-centric agent systems, Scalability in multi-
agent systems, Scale-free networks, Small-world networks.
We could solicit many high quality papers that reflect the result of the
growing recognition of the importance of the areas. All papers have received
a careful and supportive review, and we selected 19 papers out of 31 pa-
pers.Thecontributionsweresubmittedasafullpaperandreviewedbysenior
researchers from the program committee. All authors revised their earlier
versions presented at the workshop with reflecting criticisms and comments
received at the workshop. The editors would like to thank the program com-
mitteeforthecarefulreviewofthepapersandthesponsorsandvolunteersfor
their valuable contribution. We hope that as a result of reading the book you
will share with us the intellectual excitement and interest in this emerging
discipline.Wealsothankthemanyotherrefereeswhogenerouslycontributed
time to ensure the quality of the finished product.
Workshop Organizers
Satoshi Kurihara, Osaka University, Japan
Hideyuki Nakashima, Future University Hakodate, Japan
Akira Namatame, National Defense Academy, Japan, Workshop Chair
Preface vii
International Steering Committee
Robert Axtell, Brookings Institution, and Santa Fe Institute, USA
Giorgio Fagiolo, University of Verona, Italy
Satoshi Kurihara, Osaka University, Japan
Hideyuki Nakashima, Future University Hakodate, Japan
Akira Namatame, National Defense Academy, Japan
Scientific Program Committee
Robert Axtell, Santa Fe Institute, USA
Sung-Bae Cho, Yosei University, Korea
Giorgio Fagiolo, University of Verona, Verona, Italy
Kensuke Fukuda, National Institute of Informatics (NII), Japan
David Green, Monash University, Australia
Yukio Hayashi, Japan Advanced Institute of Science and Technology
(JAIST), Japan
Dirk Helbing, Dresden Technical University, Germany
KiyoshiIzumi,NationalInstituteofAdvancedIndustrialScienceandTech-
nology (AIST), Japan
Taisei Kaizoji, International Christian University (ICU), Japan
Hidenori Kawamura, Hokkaido University, Japan
Satoshi Kurihara, Osaka University, Japan
Yutaka Matsuo, National Institute of Advanced Industrial Science and
Technology (AIST), Japan
Peter Mika, Free University of Amsterdam, Netherlands
Hideyuki Nakashima, Future University - Hakodate, Japan
Akira Namatame, National Defense Academy, Japan
Denis Phan, University of Rennes, France
Jon Sakker, Australian Defense Academy, Australia
Frank Schweitzer, ZTH, Switzerland
WataruSouma,AdvancedTelecommunicationsResearchInstituteInterna-
tional (ATR), Japan
David Wolpert, NASA Ames Research Center, USA
Tokyo, Akira Namatame
November 2006 Satoshi Kurihara
Hideyuki Nakashima
Contents
Preface .. . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . v
Incremental Development of Networked Intelligence in Flocking
Behavior . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Masaru Aoyagi and Akira Namatame
Emergence and Software development Based on a Survey
of Emergence Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Joris Deguet, Laurent Magnin, and Yves Demazeau
The Impact of Network Model on Performance
of Load-balancing.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Kensuke Fukuda, Toshio Hirotsu, Satoshi Kurihara, Osamu Akashi,
Shin-ya Sato, and Toshiharu Sugawara
Auction-Based Resource Reservation Game in Small World. . . . 39
Zhixing Huang, Yan Tang and Yuhui Qiu
Navigational Information as Emergent Intelligence
of Spontaneously Structuring Web Space. . . . . . . . . . . . . . . . . . . 53
Takashi Ishikawa
From Agents to Communities: A Meta-model for Community
Computing in Multi-Agent System. . . . . . . . . . . . . . . . . . . . . . . . 67
Kyengwhan Jee and Jung-Jin Yang
x Contents
The effects of market structure on a heterogeneous evolving
population of traders. . ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Dan Ladley and Seth Bullock
Analysis on Transport Networks of Railway, Subway
and Waterbus in Japan... . . . . . . . . . .. . . . . . . . . . .............99
Takahiro Majima, Mitujiro Katuhara and Keiki Takadama
Network Design via Flow Optimization. . . . . . . . . . . . . . . . . . . . .115
Yusuke Matsumura, Hidenori Kawamura, Koichi Kurumatani,
and Azuma Ohuchi
Gibbs measures for the network ...........................129
Syuji Miyazaki
Extracting Users’ Interests of Web-watching Behaviors Based
on Site-Keyword Graph. . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Tsuyoshi Murata and Kota Saito
Topological aspects of protein networks . . . . . . . . . . . . . . . . . . . . 147
J.C. Nacher, M. Hayashida, and T. Akutsu
Collective Intelligence of Networked Agents................. 159
Akira Namatame
Using an agent based simulation to evaluate scenarios
in customers’ buying behaviour............................177
Filippo Neri
How to Form Stable and Robust Network Structure through
Agent Learning—from the viewpoint of a resource sharing
problem. . . . . .. . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
Itsuki Noda and Masayuki Ohta
Contents xi
An Evolutionary Rulebase Based Multi-agents System........203
Hiroshi Ouchiyama, Runhe Huang, and Jianhua Ma
Improvements in Performance of Large-Scale
Multi-Agent Systems Based on the Adaptive/
Non-Adaptive Agent Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
Toshiharu Sugawara, Kensuke Fukuda, Toshio Hirotsu, Shin-ya Sato
and Satoshi Kurihara
Effect of grouping on classroom communities. . . . . . . . . . . . . . . 231
Toriumi Fujio and Ishii Kenichiro
Emergence and Evolution of Coalitions in Buyer-Seller
Networks... . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .245
Frank E. Walter, Stefano Battiston, and Frank Schweitzer