Transactions on Computational Collective Intelligence VIII

236 Pages·2012·6.536 MB·English

by  Germano Resconi, Chris Hinde (auth.), Ngoc-Thanh Nguyen (eds.)

Lecture Notes in Computer Science 7430
CommencedPublicationin1973
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EditorialBoard
DavidHutchison
LancasterUniversity,UK
TakeoKanade
CarnegieMellonUniversity,Pittsburgh,PA,USA
JosefKittler
UniversityofSurrey,Guildford,UK
JonM.Kleinberg
CornellUniversity,Ithaca,NY,USA
FriedemannMattern
ETHZurich,Switzerland
JohnC.Mitchell
StanfordUniversity,CA,USA
MoniNaor
WeizmannInstituteofScience,Rehovot,Israel
OscarNierstrasz
UniversityofBern,Switzerland
C.PanduRangan
IndianInstituteofTechnology,Madras,India
BernhardSteffen
TUDortmundUniversity,Germany
MadhuSudan
MicrosoftResearch,Cambridge,MA,USA
DemetriTerzopoulos
UniversityofCalifornia,LosAngeles,CA,USA
DougTygar
UniversityofCalifornia,Berkeley,CA,USA
MosheY.Vardi
RiceUniversity,Houston,TX,USA
GerhardWeikum
MaxPlanckInstituteforInformatics,Saarbruecken,Germany
Ngoc Thanh Nguyen (Ed.)
Transactions on
Computational
Collective IntelligenceVIII
1 3
VolumeEditor
NgocThanhNguyen
WroclawUniversityofTechnology
Wyb.Wyspianskiego27
50-370Wroclaw,Poland
E-mail:[e-mail]
ISSN0302-9743(LNCS) e-ISSN1611-3349(LNCS)
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DOI10.1007/978-3-642-34645-3
SpringerHeidelbergDordrechtLondonNewYork
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CRSubjectClassification(1998):
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Preface
Welcome to the eighth volume of Transactions on Computational Collective
Intelligence (TCCI). This is the third issue in2012.This TCCI volume includes
10 interesting and original papers selected after a peer-review process.
Thefirst,entitled“IntroductiontoActiveSetsandUnification”byGermano
ResconiandChrisHinde,presentsanoriginalframeworkforanactivesetwhich
is a unifying space being able to act as a “bridge” for transferring information,
ideas and results between distinct types of uncertainties and different types of
applications. Active sets can be considered as a very useful tool for collective
intelligence.
In the second paper, “Modeling a Teacher in a Tutorial-Like System Using
Learning Automata,” the authors B. John Oommen and M. Khaled Hashem
presenta novelapproachto modeling the behaviorofa teacherina tutorial-like
system. The interesting aspect of this model is that the teacher is capable of
presenting teaching material from a Socratic-type domain model via multiple-
choice questions.
Thenextpaper,“PowerNetworkReliabilityComputationsUsingMulti-Agent
Simulation” by Ales Horak, Miroslav Prymek, and Tadeusz Sikora, presents a
multi-agent simulation system designed for modeling several aspects of power
networkelementsandpowernetworkprocesses.Owingtothisitisabletopredict
the weak points of a simulated power network over an extended time span. The
authorshaveshownthatthepredictiontakesintoaccountthenetworktopology,
meaning that each element is modeled in a context that is close to the real one.
In the fourth paper entitled “Sequence Automata for Researching Consensus
Levels,” the author, Henryk Piech, addresses the problem of building sequence
automataandusingthemtoprocessinconsistency.Theauthorproposesaunified
form of complex criteria to work out the tools for consistency estimation.
Inthepaper“AnAnalysisoftheInfluenceofFundamentalIndexingonFinan-
cialMarketsThroughAgent-BasedModeling:TheFundamentalistandFundamen-
talIndexing”byHiroshiTakahashi,ananalysisoftheinfluenceofindicesthatare
employedintheassetmanagementbusinessonfinancialmarketsthroughagent-
basedmodelingispresented.Theauthorhasfocusedonafundamentalindexbeing
proposedasanew benchmarkfor investments.Onthe basisofthis analysis,the
authorhasachievedveryinterestingresults.
Thenextpaperentitled“Agent-DrivenIntegrationArchitectureforComponent-
BasedSoftwareDevelopment,”bySodanyKiv,YvesWautelet,andManuelKolp,
presentsanarchitecturalpatternforcomponentsintegration,whichhasbeenused
inamulti-agentsystemrealizinganabstractionlayeroverthecomponents.This
multi-agentsystemallowsonetofunctionallydecomposeandcoordinateuserre-
questswithrespecttoavailablecomponents.
VI Preface
In the seventh paper with title “Adaptive Tutoring in an Intelligent Conver-
sational Agent System,”AnnabelLatham,KeeleyCrockett,DavidMcLean,and
Bruce Edmonds present an adaptive online conversational intelligent tutoring
system called Oscar that delivers a personalized natural language tutorial. Ow-
ing to the intelligent algorithms, Oscar can dynamically predict and adapt to a
student’s learning style during the tutoring conversation.
Intheeighthpaperentitled“BiologicalandComputationalPerspectivesonthe
Emergence of Social Phenomena: Shared Understanding and Collective Power,”
Jan Treur presents an approach for building computational mechanisms to ob-
tainhuman-likesocialagentmodels basedonneurologicalconcepts.The author
hasanalyzedhowthesesocialagentmodelsareindeedanadequatebasisforthe
emergenceofsharedunderstandingandcollectivepoweringroupsofagents.
Theninthpaperentitled“EgressModelingThroughCellularAutomata-Based
Multi-AgentSystems,”OlivierSzymanezyk,TomDuckett,andPatrickDickinson
presentaframeworkforbuildingaspatialdatabaseapproachandextendingitby
augmentingthesocialandenvironmentaldata.Theauthorsthenusethis model
to compute the navigational force acting on agents. They have also evaluated
and validated the feasibility of the approach.
In the last paper, “Approximate Algorithms for Solving O1Consensus Prob-
lems UsingComplex TreeStructure,”MarcinMaleszkaandNgocThanhNguyen
presentamethodfordeterminingtheintegrationofasetofdataincomplexand
hierarchical structures. They have worked out an algorithm for calculating the
consensus satisfying criterion O1, meaning that the sum of distances from the
consensus to the given data elements should be minimal.
TCCI is a peer-reviewed and authoritative journal dealing with the working
potentialofCCI methodologiesandapplicationsas wellas with emergingissues
of interest to academics and practitioners. The research area of CCI has been
growingsignificantlyinrecentyearsandweareverygratefultoeveryonewithin
the CCI researchcommunity who has supported the Transactions on Computa-
tional Collective Intelligence andits affiliatedeventsincluding the International
Conferences on Computational Collective Intelligence(ICCCI).ICCCI2012was
held in Ho Chi Minh City, Vietnam, in November 2012.After each event of IC-
CCI, we invite authors of selected papers to extend them and submit them for
publication in TCCI.
We would like to thank all the authors, the Editorial Board members, and
the reviewersfor their contributionsto TCCI. We wouldalsolike to express our
cordial thanks to the LNCS editorial staff of Springer and Alfred Hofmann for
their support of the TCCI journal.
July 2012 Ngoc Thanh Nguyen
Transactions on Computational Collective
Intelligence
This Springer journalfocuses on researchin applications of the computer-based
methods of computationalcollective intelligence (CCI) andtheir applications in
awiderangeoffieldssuchastheSemanticWeb,socialnetworks,andmulti-agent
systems.Itaimstoprovideaforumforthepresentationofscientificresearchand
technological achievements accomplished by the international community.
Thetopicsaddressedbythisjournalincludeallsolutionsofreal-lifeproblems
for which it is necessary to use CCI technologies to achieve effective results.
The emphasis of the papers published is on novel and original research and
technological advancements. Special features on specific topics are welcome.
Editor-in-Chief
Ngoc Thanh Nguyen Wroclaw University of Technology, Poland
Co-editor-in-Chief
Ryszard Kowalczyk Swinburne University of Technology, Australia
Editorial Board
John Breslin NationalUniversity ofIreland,Galway,Ireland
Shi-Kuo Chang University of Pittsburgh, USA
Longbing Cao University of Technology Sydney, Australia
Oscar Cordon European Centre for Soft Computing, Spain
Tzung-Pei Hong National University of Kaohsiung, Taiwan
Gordan Jezic University of Zagreb, Croatia
Piotr Je¸drzejowicz Gdynia Maritime University, Poland
Kang-Huyn Jo University of Ulsan, Korea
Rados(cid:4)law Katarzyniak Wroclaw University of Technology, Poland
Jozef Korbicz University of Zielona Gora, Poland
Hoai An Le Thi Metz University, France
Pierre L´evy University of Ottawa, Canada
Tokuro Matsuo Yamagata University, Japan
Kazumi Nakamatsu University of Hyogo, Japan
ToyoakiNishida Kyoto University, Japan
Manuel Nu´n˜ez Universidad Complutense de Madrid, Spain
Julian Padget University of Bath, UK
VIII Transactions on Computational Collective Intelligence
Witold Pedrycz University of Alberta, Canada
Debbie Richards Macquarie University, Australia
Roman Sl(cid:4)owin´ski Poznan University of Technology, Poland
Edward Szczerbicki University of Newcastle, Australia
Kristinn R. Thorisson Reykjavik University, Iceland
Gloria Phillips-Wren Loyola University Maryland, USA
Sl(cid:4)awomir Zadroz˙ny Institute of Research Systems, PAS, Poland
Table of Contents
Introduction to Active Sets and Unification ......................... 1
Germano Resconi and Chris Hinde
Modeling a Teacher in a Tutorial-like System Using Learning
Automata....................................................... 37
B. John Oommen and M. Khaled Hashem
Power Network Reliability Computations Using Multi-agent
Simulation ...................................................... 63
Aleˇs Hor´ak, Miroslav Pr´ymek, and Tadeusz Sikora
Sequence Automata for Researching Consensus Levels ................ 82
Henryk Piech
An Analysis of the Influence of Fundamental Indexing on Financial
Markets through Agent-Based Modeling: The Fundamentalist and
Fundamental Indexing............................................ 102
Hiroshi Takahashi
Agent-Driven Integration Architecture for Component-Based Software
Development .................................................... 121
Sodany Kiv, Yves Wautelet, and Manuel Kolp
Adaptive Tutoring in an Intelligent ConversationalAgent System ...... 148
Annabel Latham, Keeley Crockett, David McLean, and
Bruce Edmonds
Biological and Computational Perspectives on the Emergence of Social
Phenomena: Shared Understanding and Collective Power.............. 168
Jan Treur
Agent-Based Crowd Simulation in Airports Using Games Technology... 192
Olivier Szymanezyk, Tom Duckett, and Patrick Dickinson
Approximate Algorithms for Solving O1 Consensus Problems Using
Complex Tree Structure .......................................... 214
Marcin Maleszka and Ngoc Thanh Nguyen
Author Index.................................................. 229
Introduction to Active Sets and Unification
Germano Resconi1 and Chris Hinde2
1 Dept.of Mathematics and Physics,
Catholic UniversityBrescia,
I-25121, Italy
[e-mail]
2 Department of Computer Science,
Loughborough University,
Leicestershire LE11 3TU, UK
[e-mail]
Abstract. An active set is a unifying space being able to act as a
“bridge” fortransferring information, ideas andresultsbetweendistinct
types of uncertainties and different types of applications. An active set
is a set of agents who independently deliver true or false values for a
given proposition. It is not a simple vector of logic values for different
propositions, the results are a vector but the set is not. The difference
between an ordinary set and active set is that the ordinary set has pas-
siveelementswith valuesof theattributesdefinedbyan externalagent,
intheactivesetanyelementisanagentthatinternallydefinesthevalue
of a given attribute for a passive element. So agents in many cases are
inalogicconflictandthisgeneratessemanticuncertaintyontheevalua-
tion.Criteriaandagentsarethetwovariablesbywhichwegivedifferent
logic values to the same attribute or proposition. In modal logic, given
a proposition, we can evaluate the proposition only when we know the
worlds where the proposition is located. In epistemic logic any world is
an agent that knows that the proposition is true or false. The active
set is a set of agents as in epistemic logic but thedifference with modal
logic is that all the agents (worlds) are not separate but are joined in
the evaluation of the given proposition. In active set for one agent and
one criteria we have one logic value but for many agents and criteria
theevaluation is nottrueorfalse butis amatrix of trueand false. This
matrix is not only a logic evaluation as in the modal logic but give us
the conflicting structure of the active set evaluation. The agent multi
dimensional spaceto evaluateactive set also includes theHilbert multi-
dimensionalspacewhereispossibletosimulatequantumlogicgate.New
logic operation are possible as fuzzy gate operations and more complex
operations as conflicting solving, consensus operations, syntactic incon-
sistency,semanticinconsistencyandknowledgeintegration.Inthespace
of the agents evaluations morphotronic geometric operations are a new
frontier to model new types of computers. In this paper we show the
connection between classical, fuzzy, evidencetheory and active sets.
N.T.Nguyen(Ed.):TransactionsonCCIVIII,LNCS7430,pp.1–36,2012.
(cid:2)c Springer-VerlagBerlinHeidelberg2012
2 G. Resconi and C. Hinde
1 Introduction
Inthepapers[1,2,29]andinthepaper“Beliefmergingandvoting” [13]wefound
that In the voting model there are several conflicting demands/preferences and
we are looking for a collective compromise. Voting is concerned with the ag-
gregation of individual preferences in order to select a collectively preferred al-
ternative. This problem is extensively studied by social choice theory [1,2,29].
Probably the most famous method for the aggregationof preferences is the one
proposedinthe 18thcentury bythe Marquisde Condorcet[4]. Givenasetofin-
dividualpreferences,wecompareeachofthe alternativesinpairs.Foreachpair,
we determine the winner by majority voting, and the final collective ordering is
obtained by a combination of all partial results. Unfortunately, this method led
to the first aggregationproblem, knownas the Condorcetparadox:the pairwise
majority rule can lead to cycles in the collective ordering. In other words, this
ordering cannot be used to select an overall preferred candidate.
In [7] given a fuzzy set defined in a space X, let P be a representative
population of persons. For a given x that belongs to X each member of P is
askedtoacceptorrejectxassatisfyingthefuzzysetcondition.Abinarydecision
must be made. Now for a group of 10 persons, Equation 3, the voting pattern
could then be as shown in Table 2.
X ={a,b,c,d,e,f,g} (1)
with a voting pattern such as 2
⎡ ⎤
1 2 3 4 5 6 7 8 9 10
⎢ ⎥
⎢aaaaaaaaaa ⎥
⎢ ⎥
⎢ b b b b b b b ⎥ (2)
⎣ ⎦
c c c c
d d
would result in the fuzzy set 3.
f ={1.0|a+0.7|b+0.5|c+0.2|d} (3)
Now in the voting patternwe allow repetitions of the elements inX in a wayto
agree to the fuzzy set definition, we can order the elements so that the highest
count is to the left and the counts decrease towards the right, see Table 4.
⎡ ⎤
1 2 3 4 5 6 7 8 9 10
⎢ ⎥
⎢aaaaaaaaaa ⎥
⎢ ⎥
⎢b b b b b b b ⎥ (4)
⎣ ⎦
c c c c c
dd
If the elements are ordered and we grant that any voter who votes for b will
votefor a thenwe geta unique representation.This assumptionthatvoterswill
voteconsistentlyisknownastheconstantthresholdassumption[7].providedthe