Table Of ContentLecture Notes in Artificial Intelligence 1544
Subseries ofLectureNotesin ComputerScience
Editedby J.G. Carbonelland J. Siekmann
Lecture Notes in Computer Science
Editedby G.Goos,J. Hartmanisand J.van Leeuwen
(cid:0)Berlin
Heidelberg
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Barcelona
HongKong
London
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Chenqi Zhang Dickson Lukose (Eds.)
Multi-Agent Systems
Theories, Languages, and Applications
4th Australian Workshop
on Distributed Artificial Intelligence
Brisbane, QLD, Australia, July 13, 1998
Selected Papers
(cid:0) (cid:1)
SeriesEditors
JaimeG.Carbonell,CarnegieMellonUniversity,Pittsburgh,PA,USA
Jo¨rgSiekmann,UniversityofSaarland,Saarbru¨cken,Germany
VolumeEditors
ChenqiZhang
DicksonLukose
SchoolofMathematicalandComputerSciences
TheUniversityofNewEngland
Armidale,N.S.W.,2351Australia
E-mail:{chengqi,lukose}@cs.une.edu.au
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Multi-agentsystems:theories,languages,andapplications;proceedings/4th
AustralianWorkshoponDistributedArtificialIntelligence,Brisbane,QLD,
Australia,July13,1998.ChenqiZhang;DicksonLukose(ed.).-Berlin;
Heidelberg;NewYork;Barcelona;HongKong;London;Milan;Paris;
Singapore;Tokyo:Springer,1998
(Lecturenotesincomputerscience;Vol.1544:Lecturenotesin
artificialintelligence)
ISBN3-540-65477-1
CRSubjectClassification(1998):I.2.11
ISBN3-540-65477-1SpringerBerlinHeidelbergNewYork
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Preface
ThisvolumecontainsrevisedversionsofselectedpaperspresentedattheFourth
Australian Workshop on Distributed Artificial Intelligence (DAK’91), together
with a set of invited papers. Each paper has been reviewed by at least two
programcommittee members.The workshopwas held inBrisbane,Queensland,
AustraliaonJuly17,1992.Thegoaloftheworkshopwastopromoteresearchin
distributed artificial intelligence and multi-agent systems, both nationally and
internationally.Thepaperscoverawiderangeofissuesinthefieldofdistributed
artificial intelligence and multi-agent systems, such as theories, languages, and
applications.
Manypeoplecontributedtothesuccessofthisworkshop.Wewouldliketothank
allthe authorswho submittedpapers to the workshop.Manythanks alsoto the
members of the programme committee who diligently reviewed all the papers
submitted.Finally,wethankthe editorialstaffofSpringer-Verlagforpublishing
this contribution to the Lecture Notes in Artificial Intelligence series.
October 1998 Chengqi Zhang and DicksonLukose
Programme Committee
Chengqi Zhang (Co-Chair) University of New England, Australia
Dickson Lukose (Co-Chair) Brightware Inc., USA
Victor Lesser University of Massachusetts, USA
Jeffrey S. Rosenschein Hebrew University, Israel
Toshiharu Sugawara NTT Basic Research Labs, Japan
Rose Dieng ACACIA Project, INRIA, France
Norbert Glaser CRIN-INRIA Lorraine, France
Sascha Ossowski Rey Juan Carlos Univ, Spain
Michael Luck University of Warwick, UK
Mark d’Inverno University of Westminster, UK
Tim Norman University of London, UK
Douglas Norrie University of Calgary, Canada
Bernard Moulin Laval University, Canada
Zhisheng Huang University of London, UK
Minjie Zhang Edith Cowan University, Australia
Brahim Chaib-draa Laval University, Canada
Table of Contents
Team Formationby Self-Interested Mobile Agents .......................... 1
Stanisl aw Ambroszkiewicz, Olaf Matyja, and Wojciech Penczek, Poland
Conception, Behavioral Semantics and Formal
Speci(cid:12)cation of Multi-Agent Systems ...................................... 16
Walid Chainbi, Mohamed Jmaiel, and Ben Hamadou Abdelmajid, Tunisia
Learning Message-Related Coordination Control in Multiagent Systems ... 29
Toshiharu Sugawara and Satoshi Kurihara, Japan
Multi-Agent Reinforcement Learning System
Integrating Exploitationand Exploration-Oriented Learning ............... 45
Satoshi Kurihara, Rikio Onai, and Toshiharu Sugawara, Japan
Motivated Behavior for Goal Adoption .................................... 58
Michael Luck and Mark d’Inverno, U.K.
Physically Based, Self-Organizing Cellular Automata ...................... 74
Alan Dorin, Australia
Service Contract Negotiation - Agent-Based Support for
Open Service Environments ............................................... 88
Annika Waern, Sweden
An Awareness-Based Model for Agents Involved in Reporting Activities .. 105
Bernard Moulin, Canada
GISM: A Language for Modellingand Developing Agent-Based
Intelligent Systems ....................................................... 122
Hongxue Wang and John Slaney, Australia
ProgrammingInternet Based DAI Applications in Qu-Prolog ............. 137
Keith Clark, Peter J. Robinson, and Richard Hagen, Australia
Design of an Agent-Based, Multi-user Scheduling Implementation ........ 152
Andrew Scott, Karen Jenkin, and Rye Senjen, Australia
A Team of Agents Cooperating for Intelligent Tutoring ................... 166
Ruddy Lelouche, Canada
Secure Information Gathering Agent for Internet Trading ................ 183
X.F. Wang, X. Yi, K.Y. Lam, and E. Okamoto, Singapore and Japan
Author Index .......................................................... 195
Team Formation by Self-Interested Mobile
?
Agents
Stanisl awAmbroszkiewicz, Olaf Matyja,and Wojciech Penczek
Institute ofComputer Science, al.Ordona 21, 01-237 Warsaw,Poland
fpenczek,olaf,[email protected]
WWW home page: http://www.ipipan.waw.pl/mas/
Abstract. Aprocess ofteamformationbyautonomous agentsinadis-
tributedenvironment ispresented. Sincetheenvironment isdistributed,
there are serious problems with communication and consistent decision
making inside a team. To deal with these problems, the standard tech-
nique of token passing in a computer network is applied. The passing
cycle of the token serves as the communication route. It assures con-
sistent decision making inside the team maintaining its organizational
integrity. On the other hand it constitutes a component of the plan of
thecooperativeworkperformedbyacompleteteam.Twoalgorithmsfor
teamformationaregiven.The(cid:12)rstoneisbasedonsimpleself-interested
agentsthatstillcanbeviewedasreactiveagents(see[14])althoughaug-
mented withknowledge, goal,and cooperation mechanisms. Thesecond
one is based on sophisticated self-interested agents. Moreover, the algo-
rithm based on fully cooperative agents, which is an adaptation of the
static routing algorithm in computer networks, is constructed in order
to compare its performance with performances of the team formation
algorithms.
Keywords: Team formation, cooperation mechanisms, autonomous
agent, distributed computer networks.
1 Introduction
Cooperation is often considered as one of the key concepts of Multi-Agent Sys-
tems [4]. Among the cooperation mechanisms, the special interest is devoted to
the notions of team [2,7,8,23], teamwork [21,11,5], and team formation [3,10].
Usually,itis assumed inapplications(see [8,21,11])thatthe members ofa team
are (cid:12)xed and the research is focused on the teamwork. The research on team
formation concerns mainly theoretical and fundamental aspects, so that there
areveryfewrealisticmodelsofteamformationprocesses. Ourapproachtoteam
formationisquitepractical,i.e.,itisbasedonexamplesintheparticulardomain
ofdistributed computernetworks. Sincethe environmentisdistributed,itforces
allinteractions inside a team,(i.e.,informationflow,decision making,goalreal-
izing)tobe local.Thus,inorder tomaintainintegrityoftheteam,amechanism
? Partiallysupported by the StateCommitteefor Scienti(cid:12)cResearch under the grant
No.8T11C 031 10 and ESPRIT project 20288 CRIT-2
C.Zhang,D.Lukose(Eds.): Multi-AgentSystems, LNAI1544,pp. 1{15,1998.
(cid:13)c Springer-VerlagBerlinHeidelberg1998
2 Stanis law Ambroszkiewicz, OlafMatyja, and Wojciech Penczek
is needed for eliminating inconsistent decision making as well as contradictory
information and goals inside the team. To manage with all these problems we
adoptthestandardtechniqueoftokenpassinginacomputernetwork.Thetoken
is the sign of decision power, so that a member of the team who has currently
the token has got the exclusive authority to decide on the status of the team.
The passing cycle of the token serves for the following purposes: (1) It assures
thatthe decision power ispassed fromone agent toanother insidethe team.(2)
It determines the communicationroute. (3) It is acomponent ofthe planof the
cooperative work performed by a complete team. These three items correspond
to organizational,informationflow,and business structure ofan enterprise [12].
Hence, our notion of team maybe seen as a simple organizationalform.
Thecrucialideaofourapproachreliesonthefactthattheteamisexpanding
locally,starting with a single agent. If the common goalof the expanding team
becomes inconsistentaccordingtothe knowledgeacquired fromthedynamically
changing environment, then the team starts shrinking by removing some of its
members. As soon as the expanding team becomes complete, i.e., it has all the
resources and capabilities needed to achieve the common goal, the members
of the team perform the cooperative work. Another interesting feature of our
approachisthattheplanofthe(cid:12)nalcooperativeworkoftheteamisconstructed
and revised step by step during its formation. We introduce two algorithms
that correspond to di(cid:11)erent types ofself-interested agents participatingin team
formation. They are based on simple self-interested agents and ’electric’ self-
interestedagents.Simpleself-interestedagentscanbeviewedasareactiveagents
(see [14]) augmented with knowledge, goal, and cooperation mechanisms. The
strategy of simpleself-interested agents is very simple as it consists inmakinga
fusion with any team that has got a consistent goal.This results in a situation,
where the teams are expanding and shrinking very fast. The strategy of electric
self-interested agents is more subtle. It is based onthe idea ofan electric circuit
with resistors. Roughlyspeaking, the strategy determines a team to joinby the
agent, which assures maximal subjective probability (according to the agent’s
knowledge and calculations) for the agent to achieve his goal.
In order to have a basis for comparison of performances of our team forma-
tionalgorithms,we introduce the Ant-hillalgorithm(based on fullycooperative
agents called ants here) that is an adaptationof the static routing algorithmin
computer networks, see [22]. As the working example we consider a network of
servers and mobile agents that can move from one server to another in order
tolookfor resources scattered randomlyon the servers. Toenhance cooperation
betweentheagents,theentrytosomeservers isforbiddenforsomeagents.Thus,
ifaresource, wanted by an agent,is ona forbiddenserver, then the agent needs
helpfromother agents. Ifseveral agents need the same resource, then they have
tocompete forit.Agents can execute the followingtypes ofactions:take (drop)
a resource from (to) a server, receive (give) a resource from (to) another agent,
move from one server to another, or to end its activity.
Team Formation by Self-Interested Mobile Agents 3
Ourteamformationprocess maybeseenasacooperationmechanism[4]and
as an instance of a cooperation structure [6]. However, the limit of space does
not permit to discuss these issues in detail.
Sinceournotionofteamisrelated totheclassicalnotionoforganization,our
approachmaybe viewed as a(cid:12)rst step towardsunderstanding andconstruction
of mechanisms for autonomous agents to form organizations.
The rest of the paper is organized as follows. In section 2 we formalize the
distributedenvironment.Insection3instancesoftheenvironmentarepresented.
Section 4 is devoted to the concept of team and team formation. In section
5 the Ant-hill algorithm is presented. Section 6 contains two versions of our
algorithm for the self-interested agents. Section 7 contains some details of the
implementation,experimental results, and a comparison of performances of the
three algorithms.Section 8 is devoted to the (cid:12)nal conclusion.
2 Distributed Environment
In order to present the example more formally, let us consider a graph G =
(V;E)representing anetwork ofservers, where V is the set ofvertices (servers),
E (cid:18)V (cid:2)V istheset ofedges (connections). Let M =f1;2;:::;mgbe the set of
mobile agents that can move from one server to another, V (cid:18)V be a subset of
i
servers the agenticanenter, andRbe theset ofresources distributed randomly
on the servers. There is one static gate-agent on each server v, denoted also by
v, responsible for resource management on that server and for transportation
of mobile agents. Here we assume that the gate-agents are obedient, that is,
they ful(cid:12)ll the requirements of each mobile agent if this is only possible. Let
N = M [V denote the set of all the agents. Each mobile agent i is assigned
an original goal, G (cid:18) R, which consists in getting G with minimal e(cid:11)ort
i i
and then to end its activity switching to an "o(cid:11)" state. The e(cid:11)ort is related
to agent’s personal account, so that any action execution costs one unit, which
is subtracted from the account. The gate-agents are always in "on" states, and
have no goals.The followingtypes of actions can be executed by agents:
{ A mobileagent i can movefrom one server v to another v0, provided v;v0 2
V ,i.e.,theservers areavailableforhim.Thisactionisajointactionofagent
i
i and the gate-agents v and v0.
{ Agent icangivearesource r toanother agentj.Thisisajointactionofthe
agents i and j, who are in a meeting state.
{ A group of agents N0 (cid:18) N can meet, provided they are at the same server.
This is a joint action of the agents N0.
{ A mobile agent can switch to the "o(cid:11)" state (terminal state).
Passing a resource between two agents is allowed only if the agents are in a
meeting state at the same server. All the agents can ’gossip’at any opportunity
by exchanging their knowledge concerning another agents (i.e., their resources
and their sites) including themselves. Moreover, they calculate the most recent
