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Recently, the study of intelligenceemerged from interactionsamong
many agentshasbeenpopular.
Inthisstudyitisrecognizedthatanetworkstructure
oftheagentsplaysanimportantrole. Thecurrentstate-of-theartinage-
based modeling tends to be a mass of agents that have a series of
states
thattheycanexpressasaresultofthenetworkstructureinwhichtheyare
embedded.
Agentinteractionsofallkindsareusuallystructuredwithcomplex
networks. Researchoncomplexnetworksfocusesonscale-freenessofvarious
kindofnetworks.
Computationalmodelingofdynamicagentinteractionsonrichlystr-
turednetworksisimportantforunderstandingthesometimescounter-intuitive
dynamicsofsuchlooselycoupledsystemsofinteractions. Yetourtoolsto
model, understand, andpredictdynamicagentinteractionsandtheirbe-
vioroncomplexnetworkshavelaggedfarbehind. Evenrecentprogressin
networkmodelinghasnotyeto?eredusanycapabilitytomodeldynamic
processesamongagentswhointeractatallscalesonsuchassmall-worldand
scale-freenetworks. Generallythehigh-dimensional,
non-linearnatureofthe
resultingnetwork-centricmulti-agentsystemsmakesthemdi?cultorimp-
sibletoanalyzeusingtraditionalmethods. Agentsfollowlocalrulesunder
complexnetworkconstraints. Theideaofcombiningmulti-agentsystemsand
complexnetworksisalsoparticularlyrichandfreshtofostertheresearchon
thestudyofverylarge-scalemulti-agentsystems.
Weintendtoturnthisintoanengineeringmethodologytodesigncomplex
agentnetworks. Multi-agentnetworkdynamicsinvolvesthestudyofmany
agents,
constituentcomponentsgenerallyactiveoneswithasimplestructures
andwhosebehaviorisassumedtofollowlocalrules, andtheirinteractionson
complexnetwork. Abasicmethodologyistospecifyhowtheagentsinteract,
andthenobserveemergentintelligencethatoccuratthecollectivelevelin
ordertodiscoverbasicprinciplesandkeymechanismsforunderstandingand
shapingtheresultingintelligentbehavioronnetworkdynamics.
Thevolumecontainsrefereedpapersaddressingvariousimportanttopics
thataimsattheinvestigationofemergentintelligenceonnetworkedagents.
vi Preface
Especiallymostpapershighlightonthetopicssuch"networkformationamong
agents," "in?uenceofnetworkstructuresonagents," "network-basedcoll-
tivephenomenaandemergentintelligenceonnetworkedagents."
TheselectedpapersofthisvolumewerepresentedattheWorkshopon
EmergentIntelligenceofNetworkedAgents(WEIN06)attheFifthInt-
nationalJointConferenceonAutonomousAgentsandMulti-agentSystems
(AAMAS2006), whichwasheldatFutureUniversity, Hakodate, Japan, from
May8to12,2006. WEIN06isconcernedwithemergenceofintelligentbe-
viorsovernetworkedagents andfosteringtheformationofanactivemul-
disciplinarycommunityonmulti-agentsystemsandcomplexnetworks. We
especiallyintendedtoincreasetheawarenessofresearchersinthesetwo?elds
sharingthecommonviewoncombiningagent-basedmodelingandcomplex
networksinordertodevelopinsightandfosterpredictivemethodologiesin
studyingemergentintelligenceonofnetworkedagents. Fromthebroadsp-
trumofactivities, leadingexpertspresentedimportantpaperandnumerous
practicalproblemsappearthroughoutthisbook. Weinvitedhighqualityc-
tributionsonawidevarietyoftopicsrelevanttothewideresearchareasof
multi-agentnetworkdynamics. Weespeciallycoveredin-depthofimportant
areas including: Adaptation and evolution in complex networks,
Economic agentsandcomplexnetworks, Emergenceincomplexnetworks,
Emergent- telligenceinmulti-agentsystems, Collectiveintelligence,
Learningandevo- tioninmulti-agentsystems,
Webdynamicsascomplexnetworks, Multi-agent basedsupplynetworks,
Network-centricagentsystems, Scalabilityinmul- agentsystems,
Scale-freenetworks, Small-worldnetworks.
Artificial evolutionary systems are computer systems, inspired by
ideas from natural evolution and related phenomena. The field has a
long history, dating back to the earliest days of computer science,
but it has only become an established scientific and engineering
discipline since the 1990s, with packages for the commonest form,
genetic algorithms, now widely available. Researchers in the
Asia-Pacific region have participated strongly in the development
of evolutionary systems, with a particular emphasis on the
evolution of intelligent solutions to highly complex problems. The
Asia-Pacific Symposia on Intelligent and Evolutionary Systems have
been an important contributor to this growth in impact, since 1997
providing an annual forum for exchange and dissemination of ideas.
Participants come primarily from East Asia and the Western Pacific,
but contributions are welcomed from around the World. This volume
features a selection of fourteen of the best papers from recent
APSIES. They illustrate the breadth of research in the region, with
applications ranging from business to medicine, from network
optimization to the promotion of innovation.
Recently, the study of intelligenceemerged from interactionsamong
many agentshasbeenpopular.
Inthisstudyitisrecognizedthatanetworkstructure
oftheagentsplaysanimportantrole. Thecurrentstate-of-theartinage-
based modeling tends to be a mass of agents that have a series of
states
thattheycanexpressasaresultofthenetworkstructureinwhichtheyare
embedded.
Agentinteractionsofallkindsareusuallystructuredwithcomplex
networks. Researchoncomplexnetworksfocusesonscale-freenessofvarious
kindofnetworks.
Computationalmodelingofdynamicagentinteractionsonrichlystr-
turednetworksisimportantforunderstandingthesometimescounter-intuitive
dynamicsofsuchlooselycoupledsystemsofinteractions. Yetourtoolsto
model, understand, andpredictdynamicagentinteractionsandtheirbe-
vioroncomplexnetworkshavelaggedfarbehind. Evenrecentprogressin
networkmodelinghasnotyeto?eredusanycapabilitytomodeldynamic
processesamongagentswhointeractatallscalesonsuchassmall-worldand
scale-freenetworks. Generallythehigh-dimensional,
non-linearnatureofthe
resultingnetwork-centricmulti-agentsystemsmakesthemdi?cultorimp-
sibletoanalyzeusingtraditionalmethods. Agentsfollowlocalrulesunder
complexnetworkconstraints. Theideaofcombiningmulti-agentsystemsand
complexnetworksisalsoparticularlyrichandfreshtofostertheresearchon
thestudyofverylarge-scalemulti-agentsystems.
Weintendtoturnthisintoanengineeringmethodologytodesigncomplex
agentnetworks. Multi-agentnetworkdynamicsinvolvesthestudyofmany
agents,
constituentcomponentsgenerallyactiveoneswithasimplestructures
andwhosebehaviorisassumedtofollowlocalrules, andtheirinteractionson
complexnetwork. Abasicmethodologyistospecifyhowtheagentsinteract,
andthenobserveemergentintelligencethatoccuratthecollectivelevelin
ordertodiscoverbasicprinciplesandkeymechanismsforunderstandingand
shapingtheresultingintelligentbehavioronnetworkdynamics.
Thevolumecontainsrefereedpapersaddressingvariousimportanttopics
thataimsattheinvestigationofemergentintelligenceonnetworkedagents.
vi Preface
Especiallymostpapershighlightonthetopicssuch"networkformationamong
agents," "in?uenceofnetworkstructuresonagents," "network-basedcoll-
tivephenomenaandemergentintelligenceonnetworkedagents."
TheselectedpapersofthisvolumewerepresentedattheWorkshopon
EmergentIntelligenceofNetworkedAgents(WEIN06)attheFifthInt-
nationalJointConferenceonAutonomousAgentsandMulti-agentSystems
(AAMAS2006), whichwasheldatFutureUniversity, Hakodate, Japan, from
May8to12,2006. WEIN06isconcernedwithemergenceofintelligentbe-
viorsovernetworkedagents andfosteringtheformationofanactivemul-
disciplinarycommunityonmulti-agentsystemsandcomplexnetworks. We
especiallyintendedtoincreasetheawarenessofresearchersinthesetwo?elds
sharingthecommonviewoncombiningagent-basedmodelingandcomplex
networksinordertodevelopinsightandfosterpredictivemethodologiesin
studyingemergentintelligenceonofnetworkedagents. Fromthebroadsp-
trumofactivities, leadingexpertspresentedimportantpaperandnumerous
practicalproblemsappearthroughoutthisbook. Weinvitedhighqualityc-
tributionsonawidevarietyoftopicsrelevanttothewideresearchareasof
multi-agentnetworkdynamics. Weespeciallycoveredin-depthofimportant
areas including: Adaptation and evolution in complex networks,
Economic agentsandcomplexnetworks, Emergenceincomplexnetworks,
Emergent- telligenceinmulti-agentsystems, Collectiveintelligence,
Learningandevo- tioninmulti-agentsystems,
Webdynamicsascomplexnetworks, Multi-agent basedsupplynetworks,
Network-centricagentsystems, Scalabilityinmul- agentsystems,
Scale-freenetworks, Small-worldnetworks.
Understanding the mechanism of a socio-economic system requires
more than an understanding of the individuals that comprise the
system. It also requires understanding how individuals interact
with each other, and how the agg- gated outcome can be more than
the sum of individual behaviors. This book contains the papers
fostering the formation of an active multi-disciplinary community
on socio-economic systems with the exciting new ?elds of age- based
modeling and econophysics. We especially intend to increase the
awareness of researchers in many ?elds with sharing the common view
many economic and social activities as collectives of a large-scale
heterogeneous and interacting agents. Economists seek to understand
not only how individuals behave but also how the interaction of
many individuals leads to complex outcomes. Age- based modeling is
a method for studying socio-economic systems exhibiting the
following two properties: (1) the system is composed of interacting
agents, and (2) the system exhibits emergent properties, that is,
properties arising from the interactions of the agents that cannot
be deduced simply by agg- gating the properties of the system's
components. When the interaction of the agents is contingent on
past experience, and especially when the agents continually adapt
to that experience, mathematical analysis is typically very limited
in its ability to derive the outcome.
This book constitutes the thoroughly refereed joint post-proceedings of five international workshops organized by the Japanese Society of Artificial Intelligence, JSAI in 2001.The 75 revised papers presented were carefully reviewed and selected for inclusion in the volume. In accordance with the five workshops documented, the book offers topical sections on social intelligence design, agent-based approaches in economic and complex social systems, rough set theory and granular computing, chance discovery, and challenges in knowledge discovery and data mining.
Artificial evolutionary systems are computer systems, inspired by
ideas from natural evolution and related phenomena. The field has a
long history, dating back to the earliest days of computer science,
but it has only become an established scientific and engineering
discipline since the 1990s, with packages for the commonest form,
genetic algorithms, now widely available. Researchers in the
Asia-Pacific region have participated strongly in the development
of evolutionary systems, with a particular emphasis on the
evolution of intelligent solutions to highly complex problems. The
Asia-Pacific Symposia on Intelligent and Evolutionary Systems have
been an important contributor to this growth in impact, since 1997
providing an annual forum for exchange and dissemination of ideas.
Participants come primarily from East Asia and the Western Pacific,
but contributions are welcomed from around the World. This volume
features a selection of fourteen of the best papers from recent
APSIES. They illustrate the breadth of research in the region, with
applications ranging from business to medicine, from network
optimization to the promotion of innovation.
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