A Knowledge Production Protocol for Cooperative Development of Learning Objects

(1)

A Knowledge Production Protocol for Cooperative Development of Learning Objects

Juan M. Dodero

DEI Laboratory Universidad Carlos III de

Madrid

Avda. de la Universidad, 30 28911 Leganés

Madrid, Spain

dodero@inf.uc3m.es

Miguel A. Sicilia

DEI Laboratory Universidad Carlos III de

Madrid

Avda. de la Universidad, 30 28911 Leganés

Madrid, Spain

masicilia@inf.uc3m.es

Elena García-Barriocanal

Computer Science Department Universidad de Alcalá de

Henares

28871 Alcalá de Henares, Madrid, Spain

elena.garciab@uah.es

ABSTRACT

InadistributedeLearningenvironment,thedevelopmentof

learning objects becomes a cooperative task. We consider

learning objects as knowledge pieces, whichare subjectto

themanagementprocessesofacquisition,delivery,andpro-

duction. We present a two-tier architecture for coopera-

tiveknowledgeproductiontasks. Inourmodel,knowledge-

producing agents are arranged into knowledgedomains or

marts,andadistributednegotiationprotocolisusedtocon-

solidate knowledgeproduced in a mart. The architecture

canbeextended toamultiple-tierarchitecture,wherecon-

solidatedknowledgecanbenegotiatedinhigher-levelforeign

marts.Thisapproachisappliedtocooperativedevelopment

oflearningobjects,althoughitisproposedtobealsoappli-

cabletootherknowledgeproductiontasks.

Categories and Subject Descriptors

H.5.3 [Information Systems]: Group and Organization

InterfacesComputer-supportedcooperativework,Theoryand

models; K.3.1 [Computing Milieux]: Computer Uses in

EducationCollaborativelearning

General Terms

Algorithms

Keywords

Knowledgeproduction,Learningobjects,Collaborativede-

velopment,Multi-agentarchitectures

1. INTRODUCTION

Alearningobjectcanbedenedasasetoflearningcontents,

integratedwithcoursestructureand sequencing(following

LTSAterminology[2]). Inamoregeneric sense,alearning

objectisanyentity,digitalornon-digital,whichcanbeused,

re-usedorreferencedduringtechnology-supportedlearning.

Theproductionoflearningobjectsbecomesacommontask

in the industryof Internet-basedlearning services what

is often referred to as e-Learning. Inthis work, we are

consideringlearningobjectsasknowledgepieces,whichare

subjecttothemanagementprocessesofacquisition,delivery,

andproduction[16].

1.1 A Case Study of Learning Objects Devel- opment

TheIMS(InstructionalManagementSystems)GlobalLearn-

ing Consortium hasrecentlyreleased thepublic draftver-

sion1.1oftheIMSContentPackagingSpecication[7],that

providesthefunctionality todescribeand packagelearning

materials, such as an individual course or a collection of

courses,intointeroperable,distributablepackages

Somecommercialtoolkits,likeMicrosoftLRNToolkit 1

,Peer3 2

andToolBookII 3

,thatprovideimplementationsoftheIMS

Content Packaging Specication, havebeen released. Col-

laborativedevelopmentoflearningobjectsisnotsupported

insuchtools,whichpresentaunipersonalvisionofthecre-

ation,edition,viewing,andtestingoflearningobjects.

Whenconstructingalearningobject,thebuildercanusea

toollikeMicrosoftLRN,asshowningure1. Ifthelearning

objectisbeingjointlydeveloped,anotherusermaywishto

contribute,makingsomemodicationinthestructureofthe

course, or addingsomeobjectto thecoursecontents. The

IMSspecicationdenesthestructureofthelearningobject

by manifest XML les, as depicted ingure 2. Proposed

additions or modications are transformedinto changes to

themanifestle.

Whiledevelopingalearningobject,twoormoreauthorscan

cooperatewiththeexchangeofproposals,whichareimple-

mented aschangesto themanifestle. Before any change

isconsideredasconsolidated,itmustbenegotiatedbetween

the participating authors. We willrestrict our exampleto

1

http://www.microsoft.com/ele arn/s uppo rt.as p

2

http://www.peer3.com/text/so ftwar e/so ftwar e.ht ml

3

(2)

Figure1: Microsoft LRNLearningObject Editor,MicrosoftCorp.

Figure 2: IMS manifest le structure, Microsoftc

Corp.

the tableofcontents structure ofthe organizations sec-

tioninthemanifest le. Nevertheless,collaborative devel-

opmentcanbealsoextendedtoresourcesormetadatasec-

tions.

Letusconsiderthedevelopmentofalearningobjectnamed

"Introduction toXML"withMicrosoft LRNToolkit. Ina

givenmoment,anauthorknowsthecurrentlyconsolidated

knowledge(seegure3)andelaboratesanewproposal(see

gure4). There may be several negotiation processesini-

tiated, eachone aecting a section of the learning object,

resources, and metadata). Then, the author submits a

proposal, including the dierencesbetween both les, and

referingtotableofcontentsnegotiation. Therestofcollab-

oratingauthorsreceiveandevaluatetheproposal,according

toasetofpreviouslyagreedcriteria,likethosedescribedin

section 3.3. Then, a negotiation protocol is executed by

every author until the proposal is eventually accepted, or

substitutedby afurtherelaborated proposal. Thisprocess

continues until anagreement is reachedor some degree of

consensusisachieved.

1.2 Cooperative development of learning ob- jects

Sincelearningobjectscanbeconsideredasknowledge,their

cooperativedevelopmentisaprocessthatcanbeknowlegde-

managed. Knowledgemanagement canbe consideredas a

discipline that aects everyknowledgeprocess carried out

inagivenorganization(i.e.,agroupofpeoplewhoworkto-

gether). AccordingtoSwanstrom[16],knowledgeprocesses

canbebroadlyclassiedintothreecategories,thatareenu-

meratedaccordingtothecommontimingofimplementation.

Acquisition Knowledgeisasubsetofinformationthathas

been extracted, ltered and formatted in a specic

way. Oncetheusefulnessofinformationisproved,in-

formationbecomesknowledge. Thegoalofknowledge

acquisitionis toextractknowledgefromavailable in-

formationsources.

Deliveryand learning Knowledgeneedstobeconstantly

updatedanddelivered totherightplacesattheright

time. Learning is a continuous process that can be

orientedbythedeliveryofknowledgeinterestingtoa

personorcommunityofusers.

Production Knowledgebecomesanetworkofideas,plans,

(3)

(4)

users. Acooperationmodelisadvisabletocoordinate

suchknowledge production tasks, andto ensurethat

knowledgeproductioneortisnotduplicated.

Inahighlydistributedenvironmentforinstance,thefac-

ultystainavirtualuniversity,cooperativedevelopment

oflearningobjectsbecomesharder,sincetheholdingofsyn-

chronousphysical or virtualmeetings isquite lessfre-

quent. Theexchangeofideasbetweenmembersof thedis-

tributedworkgroupisanasynchronousprocess,wherepar-

ticipantsmay keeptheir ownpace within theinterchange.

Although,agroupofagentscanjointly,asynchronouslyde-

velop learning objects iftheycoordinate their creation ac-

tivities.

An structured model of cooperation between knowledge-

producing agents is presented in section 2. Inour model,

agentsarearrangedintocooperativeknowledgemarts,that

aredescribedinsection2. Knowledgeproductioninamart

isachievedbyargumentation-basednegotiation(section3).

Section 4 presents the distributed negotiation protocol to

coordinateknowledge-producingagents,and outlinessome

implementationissues.Finally,conclusionsandfuturework

aredescribedinsection5.

2. COOPERATIVE KNOWLEDGE PRODUC- TION ARCHITECTURE

Knowledge-producing agents can operate into disjoint do-

mainsorknowledgemartsthatwecallzocos 4

,as shownin

gure5. An agent canoperate inaforeign marketby us-

inganintermediateproxyagent. Proxyagentsactasmart

representativesinforeignmarts,accordingtotheproxy de-

sign pattern [5], so that interaction between marts is not

tightlycoupled. Tofacilitatecooperationbetweendomains,

martscanbe structured inahierarchical way, as depicted

ingure6.

Knowledge Mart A Collaborative

Agent

Knowledge Mart B

Knowledge Mart C Collaborative

Agent

Proxy Agent

Collaborative Agent

Collaborative Agent Proxy

Agent Proxy

Agent

Figure5: Cooperative knowledge marts

Inthisarchitecture,cooperationdomainscanbemodeledas

knowledgemarts,andmartscanbearrangedintoknowledge

warehouses.

Cooperative knowledge mart ACooperativeKnowledge

4

Zoco is the spanishword for souk, anopen-airmarketin

Cooperative Knowledge Mart Cooperative Knowledge Mart

Cooperative Knowledge Warehouse Supervisor

Agent Supervisor

Agent

Supervisor Agent

Figure6: Cooperative knowledgewarehouse

Mart(CKM)isadistributedgroupofcooperativeagents

thattrytoproduceapieceofknowledgeinagivendo-

main. Whenknowledgeproducedinamartcanaect

agents'performance insomeother domain, aspecial

proxyagentcanactasrepresentativeinaforeignmart.

Cooperative knowledgewarehouse ACooperativeKnowl-

edgeWarehouse(CKW)istheplacewhereknowledge

produced inforeign marts is merged in astructured

fashion. TwoormoreCKMscannegotiateusingrep-

resentativesinacommonCKW.

Becauseofsimplicity,wedeneatwo-tiercooperativework

architecture. Nevertheless, it can be easily extended to a

multiple-tierarchitecture,where cooperative agentscollab-

orateinlower-leveldomainstoconsolidatesomeknowledge,

beforetheytrytocollaborateorcompeteinhigher-leveldo-

mains.

Collaborativedevelopmentofa numberof learningobjects

thatmakeupanin-developmentcurriculumisnotatrivial

task. Changes or additions insome learning material can

aectseveralcoursesthatdependonit. Sometimes,twoor

moreteachingstamembershavetonegotiatetheinclusion

ofsomecontentinagivencourseunderhis/herresponsibil-

ity. Inthiscase,thearchitectureofseveralmarts,depicted

ingure6canbehelpfultostructurethenegotiation.

3. AGENT NEGOTIATION IN A KNOWL- EDGE MART

Negotiationisanessentialkindofinteractioninmulti-agent

knowledgesystems. Agentsdonot usuallyenjoy aninher-

ent control overeachother. Thus, the usual way toinu-

enceoneanotheris persuasion. Insomecases,apersuadee

agent needs a few arguments to behave according to the

persuaderdirectives. Inothercases,thepersuadeeishardly

determinedtoacceptpersuader'sproposals. Then,theper-

suadee has tobeconvincedto change itsbeliefs, goals, or

preferences, in orderto accept perhaps modied pro-

(5)

buildand deliver proposals, which canbe accepted or re-

jected. An example is the Contract Net Protocol (CNP)

[15]. Thecollaborationprotocolismoresophisticatedwhen

recipientshaveachanceto buildcounterproposalsthat al-

tercertain issuesthat werenotsatisfactory intheoriginal

proposal[13]. Amoreelaboratedformofnegotiationallows

partiesto sendjustications or argumentsalongwith pro-

posals. Sucharguments indicatewhy proposals should be

accepted[17][11][12].

Thissection introducesan approachto design argumenta-

tivemulti-agentarchitectures,whereagentstrytoconvince

each other to accept a given knowledge in some domain.

Agents arestructured into knowledgemarts,thusbuilding

upknowledgedomains. Theaimistoallowagentstoconsol-

idate knowledgecontinuouslyproducedinaCKM.Knowl-

edgeconsolidationinaCKMistheestablishmentofknowl-

edge as accepted by every agent operating in the CKM.

Agents can reach aconsensus on the CKM-wideaccepted

knowledgebytheexchangeofmessages.

3.1 Negotiation Principles

Negotiationbetweenagentsiscarriedoutbyexchangingpro-

posalsinacommonlanguage,likeACL[10]. Proposalinter-

changeisdirectedbygoalsandnecessitiesoftheparticipat-

ing agents. Althoughthe formalisation of agents'commu-

nication languageand goalsare not includedas objectives

of our model, this account is subject to a minimal set of

conventionsaboutthelanguageandnegotiationprotocol:

1. Agentrationalityismodeledintermsofpreferencere-

lationshipsorrelevancefunctions[4],inordertoallow

agentstoevaluateandcompareproposals.

2. Relevant aspectsofnegotiationcanbemodeledas is-

sues and values that can be changed as negotiation

progresses.

3. Agents can deliberate and achieve aninternal state,

thusrecordingthehistoryofnegotiationandtheirde-

cisions.

Anagent canbe involved inseveral negotiation processes.

Thenegotiationprotocoldescribedinsection4keepsasep-

aratenegotiationwhereagentscanparticipate.

3.2 Messages

The basic typesof messages abstractions that can be ex-

changed between agents belonging to the same CKM are

thefollowing:

proposal(k,n) Givenanegotiationprocessn,agentssenda

proposalmessagewhentheyhaveapieceofknowledge

k andwishittobeconsolidatedintheCKM.

consolidate(k,n) Agentssendaconsolidatemessagewhen

theyreachagivenstateinthenegotiationprotocol,for

apreviouslysubmittedownproposalktobeaccepted

rameters in above messages, since they are concern of an

underlyingtransportprotocolthatguaranteesareliablede-

livery. Aswell,messagedeliverytoeveryagentinthesame

CKM hasto besupportedbysome multicastingfacility in

theunderlyingtransport.

3.3 Proposal Relevance

Asstatedabove,agentsrationalityneeds tobemodeledin

terms ofpreference relationshipsor relevance functions,in

order to allow agents to evaluate and compare proposals.

Linguistic-expressed preferences [6] can also be integrated

innegotiation processes,asproposedin[1].

Next,wegivesomedenitionsusedintheprotocoldescribed

insection4.

Proposal attributes Proposal attributes are elementary

criteriatobeconsideredwhencomparingproposalsin

aCKM.Someexamplesofproposalattributesare:

Submitter'shierarchicallevel,usefulwhenagents

present dierent decisionprivileges intheCKM

about theacceptanceof proposals(v.g., lecturer

vs. assistantinafacultysta).

Degreeoffullmentofasetofgoals. Forinstance,

before the development of a learning content, a

set of educational objectives should be dened.

Inthecase ofcorporatelearning,thesegoalscan

beconductedbythetrainingneedsoftheorgani-

zation.

Timestampofthe momentwhenaproposalwas

rstly submitted inthe CKM (normally consid-

eredinthelastcase,whennootherattributecan

decide).

Proposal relevance Therelevanceofaproposalisdened

asthesetofproposalattributesconsideredatthemo-

mentofnegotiation.

Proposal relevancefunction Therelevancefunctionu(k)

ofaproposalk inaCKMreturns anumerical value,

dependent on attributes of k, in such a way that if

ki6=kj,thenu(ki)6=u(kj).

Proposal preferencerelationship Aproposalk1 ispre-

ferredtoanotherk

2

inaCKM,denotedask

1 k

2 ,if

u(k

1 )>u(k

2 ).

4. NEGOTIATION PROTOCOL IN A CKM

Let A

M

= fA

1

;:::;A

n

g be a discrete set of cooperative

agents,participatinginaknowledgemartM.

Rule 1 WhenA

i

wantsaknowledgepiecek tobeconsoli-

datedinM, itsendsaproposal (ki;n)toeveryagent

inM, initiating a newnegotiation n. Then, Ai sets

a timeout t

0

before conrming its proposal. During

t0,messagescanarrivefromanyotheragentAj,with

(j6=i),consistingofnewproposalsmaybetheorig-

inal,thoughmodiedreferringtothesamenegotia-

(6)

duringt

0

,itconsidersthatthereisnoagentagainstits

proposalandittriestoratifyitsproposal,bysending

aconsol idate(ki;n)toeveryagentinM.

Rule3 IfAireceivesaproposal (kj;n)messagefromother

agentA

j

,referringtothesamenegotiationn,A

i eval-

uates thenewproposalk

j . Ifk

i k

j

,thenA

i setsa

newtimeoutt1,waitingforproposalkjtoberatied.

Then,A

i

proceedsasfollows:

1. If Ai does not receive any proposal referred to

negotiation nbeforet

1

expires,thenA

i

initiates

back the protocol inRule 1with the same pro-

posalki.

2. IfA

i

receivesaconsol idate(k

j

;n),withk

j k

i ,

for j6=i, beforet1 expires,and referringto the

samenegotiation n, thenAi givesup the initial

proposalandtheprotocolnishesunsuccessfully.

3. IfAireceivesanewproposal (kj;n),withkikj,

itextendsthetimeoutt1.

Weareconsideringtwodierenttimeouts,oneforeachphase

thatcanbenoticedinthenegotiationprocess.T0isusedfor

thedistributionphase,thatoccursafteranagentsubmitsa

proposalbyexecutingRule1. T1 isusedfortheconsolida-

tionphase,thatoccursifanagentthatisinitsdistribution

phase(t0-waiting) receives aproposal that is evaluated as

preferred.

In any moment, the reception of a message from another

agent may provoke a momentary retraction from a previ-

ouslysubmittedproposal, untilacounter-proposalis elab-

orated. An agent that has not reached this state will be

waiting for t

0

timeout. Then, if the agent receives a pro-

posalthatisevaluatedaspreferred,anewtimeoutt1 isset

to give it a chance. But if the preferred proposal is not

eventually ratied, then the agent goes onabout its aims

andwilltryagaintoconsolidateitsownproposal.

AcooperativeagentA

i

canparticipateinseveralnegotiation

processes. Each negotiation process is handledseparately,

byinitiatinganewexecutionthreadoftheprotocol.

4.1 Activation Events

Inanymoment,anagentAicanbeinvolvedinanegotiation

due to the arrival of a message from A

j

referring to the

samenegotiation. Thefollowing rules describethe actions

toundertakebyagent Aj whenit receivesamessage from

anotheragentA

j .

IfAireceivesaproposal (kj;n)fromAj:

Rule A If Ai sent a proposal (ki;n) and is waiting

for t

0

timeout, then it can perform one of the

followingactions:

Ifkj ki,thenAiactsinthesamemanner

as instep3and waits for proposalk

j tobe

ratiedoranalternativeproposaltocome.

Ifkjki,thenAisendslastproposalitsent

in the negotiation process back to A

j , and

extendst timeout.

t

1

timeout,thenitcanperformoneofthefollow-

ingactions:

Ifk

j k

i

,thenA

i

actsinthesame manner

asinstep3-3andwaitsforproposalkjtobe

ratied.

in the negotiation process back to Aj, and

extendst1 timeout.

Rule C IfA

i

didnotsentanymessagereferredtothe

samenegotiationn,itdoesnothing.

IfAireceivesaconsol idate(kj;n)fromAj:

Rule A If A

i

sent a proposal (k

i

;n) and is waiting

for t

0

timeout, then it can perform one of the

following actions:

If kj ki, then Ai acts in the same man-

ner as in step 3-2 and the protocol nishes

unsuccessfully.

in the negotiation process back to A

j , and

extendst

0

timeout.

Rule B IfA

i

sentaproposal (k

i

;n)andiswaitingfor

t1 timeout,thenitcanperformoneofthefollow-

ingactions:

If kj ki, then Ai acts in the same man-

ner as in step 3-2 and the protocol nishes

unsuccessfully.

Ifkjki,thenAi actsinthesame manner

asinstep3-1andinitiatesbacktheprotocol

withthesameproposal.

Rule C IfAididnotsentanymessagereferredtothe

samenegotiationn,itdoesnothing.

4.2 Protocol Variants

Thenegotiationprotocoldescribedaboveusesonlytwomes-

sage types(i.e., proposal and consolidate). Some variants

usingadditional messagetypescanalsobeformulated:

retract(k,n) Agentscanretractfromapreviousproposal

kbyissuingthismessagereferredtoanegotiation n.

substitute(k

1 ,k

2

,n) Agents canreplace a previouslyis-

suedproposalk

1

byanewproposalk

2

. Itisequivalent

toretract(k1;n)followedbyproposal (k2;n).

reject(k,n) Agentscanexpresswiththismessagetheirre-

fusal for aproposal k without necessity to formulate

andissueanewproposal.

4.3 Implementation

We havebuiltaprototypeimplementationoftheprotocol,

that usesmessage multicasting facilities providedby Java

Aglets [9]developmentframework. Intheend-userversion

of theprotocol,messages will bedelivered by e-mail,with

parameters codedinto theSMTP messageheader. A con-

currentversioningsystemwilltrackthechangesandcommit

theconsolidatedproposalsintoacommonrepository.

(7)

5. CONCLUSIONS AND FUTURE WORK

Thisworkpresentsamodeltodevelopacollaborativemulti-

agentarchitecture,appliedtocollaborative developmentof

learning objects. There are currentlysomepopularproto-

cols ofcooperation knowledgeused heavily by multi-agent

systems.Theseprotocolscanbeclassiedintothefollowing

approaches:

Top-downmethodologiestrytodesigndomainspecic

agent systems, either from a market-basedapproach

(v.g.,thecontractnetprotocolorCNP[14]),orfrom

anorganizationalapproach(v.g.,thefacilitatorproto-

col or FP [3]). Withtop-downmethodologies, itbe-

comesdiculttoseparatecooperationlevelknowledge

from problem-solving domain level knowledge. Jen-

nings[8] claimedtheexistenceofasociallevelknowl-

edgethatprovidesanabstractframeworkforcompar-

ingandanalyzingalltypesofmulti-agentsystems.

Bottom-upmethodologiesaim togeneratefamiliesof

componentsthatcanbeassembledtobuildcollabora-

tive agentsystemsinamorereusablefashion. When

themembersofamulti-agentsystemarescatteredover

averylargescopeontheInternet,YuanandWu[18]

sector theminto fewterritories andduplicate theso-

cial level knowledgein eachterritory. Thisapproach

couldleadtotheproblemofinconsistency.

Thearchitecturepresentedhereisabottom-upapproachto

thedesignofcooperativemulti-agentsystems.EveryCKM

holdsresponsibilitiesonsomedomainlevelknowledge,while

cooperationlevelknowledgeinterfacestootherdomainsare

well-dened. Thestructuring ofknowledgemartscanhelp

toreduceinconsistencies betweenagentterritories. Theco-

operativeapproachpresentedinthisworkisalsoapplicable

to other knowledge production tasks, as software develop-

ment,speciallyinanalysisanddesignphases. Nevertheless,

furthervalidationis neededto assessthe usefulnessof the

protocolindierentscenarios, andweare conductingtests

onthe impactof thenumberofagents inthe overalleec-

tivenessofthemodel. Ourworkdoesnotconsideryethow

knowledge martsare set up. Asafuturework, knowledge

mart generation and the participation of agents in CKMs

areproposedtobedynamic,dependingonagents'ontology-

basedexpressedinterests.

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