Table Of ContentLecture Notes in Computer Science 6560
CommencedPublicationin1973
FoundingandFormerSeriesEditors:
GerhardGoos,JurisHartmanis,andJanvanLeeuwen
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 Intelligence III
1 3
Editor-in-Chief
NgocThanhNguyen
WroclawUniversityofTechnology
InstituteofInformatics
Str.Wyb.Wyspianskiego27
50-370Wroclaw,Poland
E-mail:[email protected]
ISSN0302-9743(LNCS) e-ISSN1611-3349(LNCS)
ISSN2190-9288(TCCI)
ISBN978-3-642-19967-7 e-ISBN978-3-642-19968-4
DOI10.1007/978-3-642-19968-4
SpringerHeidelbergDordrechtLondonNewYork
LibraryofCongressControlNumber:2011923804
CRSubjectClassification(1998):I.2,C.2.4,I.2.11,H.3-5,D.2
©Springer-VerlagBerlinHeidelberg2011
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Preface
Welcome to the third volume of Transactions on Computational Collective In-
telligence (TCCI). It is the second year of this new Springer journal which is
devoted to the researchin computer-based methods of computational collective
intelligence (CCI) and their applications in a wide range of fields such as se-
manticweb,socialnetworksandmulti-agentsystems.TCCIstrivestocovernew
methodological,theoreticalandpracticalaspectsofCCIunderstoodastheform
of intelligence that emerges from the collaborationand competition of many in-
dividuals (artificial and/or natural). The application of multiple computational
intelligence technologies such as fuzzy systems, evolutionary computation, neu-
ral systems, consensus theory, etc., aims to support human and other collective
intelligence andto createnew forms of CCI innaturaland/orartificialsystems.
TCCI is a double-blind refereed and authoritative reference dealing with
working potential of CCI methodologies and applications, as well as emerging
issuesofinterestto academicsandpractitioners.This issue containsa collection
of 10 articles selected from high-quality submissions addressing advances in the
foundations and applications of computational collective intelligence.
The research area of CCI has significantly been growing in recent years and
we are very thankful to everyone within the CCI research community who has
supported the Transactions on Computational Collective Intelligence, and its
affiliatedevents including the International Conferences on Computational Col-
lectiveIntelligence:Technologies andApplications(ICCCI).ThefirstICCCIwas
held in Wroclaw, Poland in Oct. 2009. ICCCI 2010 was held in Taiwan in Nov.
2010 and ICCCI 2011 will be hosted in Gdynia, Poland in Sep. 2011. With
this strong support and a large number of submissions we are very pleased that
TCCI and ICCCI are being cemented as high-quality platforms for presenting
and exchanging most important and significant advances in CCI research and
development. We would like to thank all the authors for their contributions to
TCCI. This issue would also not have been possible without the great efforts
of the editorial board and many anonymously acting reviewers.Here, we would
like to express our sincere thanks to all of them. Finally, we would also like to
expressourgratitudetotheLNCSeditorialstaffofSpringer,inparticularAlfred
Hofmann,Ursula Barth,PeterStrasserandtheir team,who havesupportedthe
TCCI journal and the editorship of this issue in a very professionalway.
February 2011 Ngoc Thanh Nguyen
Transactions on Computational Collective
Intelligence
Thisnewjournalfocusesonresearchinapplicationsofthecomputer-basedmeth-
ods of Computational Collective Intelligence (CCI) and their applications in a
wide range of fields such as semantic web, social networks and multi-agent sys-
tems. It aims to provide a forum for the presentation of scientific research and
technologicalachievements accomplished by the international community.
Thetopicsaddressedbythisjournalincludeallsolutionsofreal-lifeproblems,
forwhichitisnecessarytousecomputationalcollectiveintelligencetechnologies
toachieveeffectiveresults.Theemphasisofthepaperspublishedisonnoveland
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 National University of Ireland, 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 Jedrzejowicz Gdynia Maritime University, Poland
(cid:2)
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
VIII Transactions on Computational Collective Intelligence
Manuel Nu´n˜ez Universidad Complutense de Madrid, Spain
Julian Padget University of Bath, UK
Witold Pedrycz University of Alberta, Canada
Debbie Richards Macquaire 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
Cooperation of Agents Based on Methods of DES Supervising and
Control......................................................... 1
Frantiˇsek Cˇapkoviˇc
Role of Thesauri in the Information Management in the Web-Based
Services and Systems ............................................. 25
Tomasz Kubik
On Efficiency of Collective Intelligence Phenomena ................... 50
Tadeusz (Ted) Szuba, Pawe(cid:2)l Polan´ski, Pawel(cid:2) Schab, and
Pawe(cid:2)l Wielicki
Enhancing the Computational Collective Intelligence within
Communities of Practice Using Trust and Reputation Models.......... 74
Iulia Maries and Emil Scarlat
Loki – Semantic Wiki with Logical Knowledge Representation ......... 96
Grzegorz J. Nalepa
Rule Extraction Based on Rough Fuzzy Sets in Fuzzy Information
Systems ........................................................ 115
Ming-Chang Lee and To Chang
Patterns in World Dynamics Indicating Agency...................... 128
Tibor Bosse and Jan Treur
Agent-Based Modelling of the Emergence of Collective States Based on
Contagion of Individual States in Groups ........................... 152
Mark Hoogendoorn, Jan Treur, C. Natalie van der Wal, and
Arlette van Wissen
Head-On Collision Avoidance by Knowledge Exchange under RAF
Control of Autonomous Decentralized FMS.......................... 180
Hidehiko Yamamoto, Takayoshi Yamada, and Katsutoshi Ootsubo
Quality Assessment of an Expert System: An Instrument of Regular
Feedback from Users ............................................. 199
Barbara Begier
Author Index.................................................. 215
Cooperation of Agents Based on Methods of
DES Supervising and Control
Frantiˇsek Cˇapkoviˇc(cid:2)
Instituteof Informatics, Slovak Academy of Sciences
Du´bravsk´acesta 9, 845 07 Bratislava, Slovak Republic
[email protected]
http://www.ui.sav.sk/home/capkovic/capkhome.htm
Abstract. The cooperation of autonomous agents is synthesized in or-
der to satisfy demands imposed on a group of agents or on MAS (multi
agent systems). Petri nets (PNs), especially place/transition PNs (P/T
PNs), are used for modelling the agents. The modular approach is uti-
lized on that way. PN-based methods of discrete event systems (DES)
control and supervising are utilized in order to synthesize the coopera-
tion of the agents. The usefulness and applicability of the approach is
demonstrated by examples and case studies.
Keywords: Agents, bipartite graphs, control, cooperation, discrete-
event systems, Petri nets, supervisor, synthesis.
1 Introduction
Discrete-event systems (DES) are widely used in human practice - e.g. flexible
manufacturingsystems,communicationsystemsofdifferentkinds,transportsys-
tems,etc.Theyaresystemsdrivenby discreteevents.Thus,the DESbehaviour
isdiscreteinnature.Thebehaviourofagentscooperatingeachothercanalsobe
understood to be a kind of DES. There are many problems in the cooperation
of autonomous agents in order to achieve a prescribed global aim. Main aim
of this paper is to point out how the methods of DES control theory can be
utilized onthatway.The supervisorto be synthesizedby means ofthe methods
represents the instrument how to realize the cooperation. The causality in the
DES behaviour was described in [1]. DES are frequently modelled and analysed
by means of Petri nets (PNs), especially P/T PNs (place/transition PNs). The
control synthesis for DES modelled by PNs can be performed also in virtue of
P/T PN-based methods. In [1] the method of such a kind was introduced. It
is based on ordinary reachability graphs (RGs) and yields the space of feasible
statetrajectoriesfromagiveninitialstateto adesiredterminalone.Thispaper
immediately goes on to solve the DES controlsynthesis problemin order to au-
tomate this process as soon as possible, even to achieve the automated control
(cid:2) Partially supported by the Slovak Grant Agency for Science (VEGA) under grant
# 2/0075/09.
N.T.NguyenandR.Kowalczyk(Eds.):TransactionsonCCIIII,LNCS6560,pp.1–24,2011.
(cid:2)c Springer-VerlagBerlinHeidelberg2011
2 F. Cˇapkoviˇc
synthesis. The earlier results presented in [2],[3] are also utilized in this paper.
The original contribution of this paper on the basis of the up-to-date papers
consists in giving the methodology how to automate the process of the super-
visor synthesis. Starting from PN models of the autonomous agents a step to
a supervisor synthesis is performed. The behaviour of such a supervised agents
is analyzed (as to satisfying demands) by the method published before. When
the behaviour is not satisfied (e.g. because of some absurdities from practical
point of view, deadlocks, etc.) an additional demands are imposed on the su-
pervisedagentsandanadditionalsupervisoris synthesized.Even,we canspeak
aboutthehierarchicalapproach.Althoughitcanhardlybespokenabouta’self-
organization’, such an approach is very useful especially in case of ’material’
agents, e.g. in manufacturing systems. To illustrate such an approach two case
studies are introduced.
In the denotation from system theory, the P/T PN-based model of DES can
beformallyexpressedasthelineardiscretesystemconstrainedbytheinequality
as follows
x =x +B.u , k =0, ..., N (1)
k+1 k k
B=GT −F (2)
F.u ≤x (3)
k k
where k is the discrete step of the dynamics development; x = (σk ,...,σk )T
k p1 pn
is the n-dimensional state vector; σk ∈ {0,1,...,c }, i = 1,...,n express the
pi pi
states of the elementary subprocesses or operations by 0 (passivity) or by 0 <
σ ≤ c (activity); c is the capacity of the subprocess p as to its activi-
pi pi pi i
ties; u = (γk,...,γk )T is the m-dimensional control vector; its components
k t1 tm
γk ∈ {0,1}, j =1,...,m represent occurring of elementary discrete events (e.g.
tj
starting or ending the elementary subprocesses or their activities, failures, etc.)
by 1 (presence of the corresponding discrete event) or by 0 (absence of the
event); B, F, G are matrices of integers; F = {f } , f ∈ {0,M }, i =
ij n×m ij fij
1,...,n, j = 1,...,m expresses the causal relations among the states (as causes)
andthediscreteeventsoccurringduringtheDESoperation(asconsequences)by
0(nonexistenceoftherelation)orbyM >0(existenceandmultiplicityofthe
fij
relation); G = {g } , g ∈ {0,M }, i = 1,...,m, j = 1,...,n expresses
ij m×n ij gij
very analogicallythe causal relations among the discrete events (as causes) and
theDESstates(asconsequences);thematrixBisgivenbymeansofthearcsin-
cidencematricesFandGaccordingto(2);(.)T symbolizesthe matrixorvector
transposition.
2 DES Control Problem
Considering the introduced system (1)-(3) to be the model of DES, the prob-
lem of control is the problem of finding a suitable sequence of control vectors
{u0, u1, ..., uN−1}transferringthesystemformagiveninitialstatex0 toapre-
scribed terminal state x . In any step k, i.e. in any state x of the system, it is
t k
Cooperation of Agents Based on Methods of DES Supervisingand Control 3
x(cid:2)k
1uk(cid:2) (cid:5)3uk
(cid:2)(cid:2)(cid:3)(cid:2)(cid:2) (cid:2)(cid:4)2u(cid:5)k (cid:5)(cid:5)(cid:6)(cid:2)
1xk+1 2xk+1 3xk+1
Fig.1. The example of alternative courses of the DES states development
necessaryto findpossiblealternativesofthe further course.Inthe PNterminol-
ogy,itisnecessarytofindPNtransitionswhichareenabledinthestepk.Inany
step k it is possible to proceed on the base of the simple logical consideration:
“if we are not able to detect directly which transitions are enabled in the step
k, but we are able to detect only which transitions are disabled in this step, we
can eliminate the disabled transitions”. This can be mathematically described
as follows
u =neg(FT.neg(x )) (4)
k k
where neg(.) formally represents the operator of negation. The applying of
this operator for a vector v = (v , v , ..., v )T of integers yields the vector
1 2 n
neg(v) = w = (w , w , ..., w )T. If v = 0 then w = 1, otherwise w = 0. But
1 2 n i i i
simultaneously,thecondition(3)hastobesatisfied.However,becausethedevel-
opmentofthePN-basedmodelisconsecutive(onlyonetransitioncanbefiredin
the step k), in case when several transitions are enabled in the step k, only one
of them can be chosen to be fired. When the vector u contains more than one
k
non-zero entry,it often does not met (3). But when such u is decomposed into
k
a set of vectors iu , i = 1, ..., r, each with the different single non-zero entry,
k
any single vector has to satisfy (3) in order to be used as the control vector.
It means that there exists only one way how to proceed from the existing state
x to another one x - see illustration in Fig. 1. Every iu , i = 1, 2, 3 in
k k+1 k
Fig. 1 has to contain only one non-zero entry. Hence, if u contains in general
k
r non-zero entries (i.e. when r transitions are enabled in the step k), theoreti-
callytherearerpossibilitiesofthefurtherdevelopmentofthesystemdynamics.
Thus,uk hastobedecomposedinto(cid:2)rcontrolvectorsiuk,i=1, ..., rwithsingle
non-zero entry in such a way that r iu = u . Thus, the branching makes
i=1 k k
such a procedure too complicated. Moreover, we do not know which way is a
part of trajectory leading to the prescribed terminal state. In general, without
additional “backtracking” information we are not able to find solution of the
control problem by means of such a “blind” procedure. Therefore, it is better
(andsimplerin general)to compute the reachabilitytree(RT) correspondingto
P/T PN-based model. The RT yields information about the branching process
inthe whole.However,eventhe RT doesnotgiveus directly anysolutionofthe
controlproblem.Namely,wehavetofindthetrajectory(ortrajectories)between
the concrete states from the pair (x , x ) and to “extract” the corresponding
0 t
trajectory (or trajectories) from the global RT. In case of large scale RTs it is
notanysimpleproblem.Namely,ingeneralthe terminalstatecanbe amultiple
leaf of the tree, i.e. there exist many trajectories from x to x . Consequently,
0 t
