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TAB LE O F C O N T E N T S

I.N T R O D U C T I O NA .R E A O FR E S E A R C HB .E S E A R C H Q U E S T I O N SC .E F I N I T I O N SA N D S C O P E OFR E S E A R C HD .E S E A R C H M E T H O D O L O G YE .R G A N I Z A T I O N OFS T U D Y

II. I T E R A T U R E R E V I E WA .N T R O D U C T I O NB .EFINITIONS

1 .g e n t2 .obileA g e n t3.ntel l igentA g e n t

C .I S T O R Y O FS OF TW A R E A G E N T SD .E N E R I C S O F T W A R E A G E N T A R C H I T E C T U R E1 .ser2 .uthor3.ifetime 84.c c o u n t5.oal6 .ubject DescriptionE .E A S O N S FO R U S I N G I N T E L L I G E N T S OFT W A R E A G E N T S01 .u n d a n ePersonal A ctivi ty 0

Vll

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3.esource usage 0 4.avigat ion 05.rivacy 0 6 .ommunicat ion 17.ontrol 1I. T E C H N I Q U E SO R L A U N C H I N G I N T E L L I G E N T S O F T W A R E A G E N T S 1

J .E X T R E T R I E V A L A N D D O C U M E N T M A N A G E M E N T ISSU ES2 1 .ata Filtering 2 2.ata Fusion 3

III.A C K G R O U N D A N D C O N C E P T S 5A .E M O T E P R O G R A M M I N G 5

1 .obileA g e n t s 6 2.lace 6 3.ravel 74.icket 8 5.gent Accounts 8 6.eetings 07.onnect ions 08.uthorities 19.egion 110 .ermits 211 .l lowance 2

B .H E Q U E R Y F O R M A T 4C .G E N T E N V I R O N M E N T 5

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IV .E C U R I T Y 7A .E F I N I T I O N S 7

1 .ecrecy 72 .ntegrity 83.on-repudiation 84.o m m u n i c a t i o n sSecurity 85.vailabili ty 86 .uthenticity 9

B .E C U R I T Y IN A G E N T B A S E D I N F O R M A T I O N S Y S T E M S11 .hreat to Servers 12 .hreats to A g e n t s 2 3.nformat ionSecurity 34.ransport N e t w o r k 4 C .P R O T O C O L FO R S E C U R E A G E N T / S E R V E R I N T E R A C T I O N4 1 .erver to Server Interaction 52 .edirection and Interceptionof Mobile A g e n t s6

V . F R A M E W O R K FO R A G E N T - B A S E DD E C I S I O N S U P P O R T :HE M O B I L E A G E N T R E C O N N A I S S A N C E K IT ( M A R K ) 9

A .O N C E P T O FS Y S T E M O P E R A T I O N 2 1 .h eClient 32 .heS e r v e r 6

B .N F O R M A T I O N F LOW 6V I.M A N A G I N G M A R K S Y S T E M P E R F O R M A N C E 5

A . C O N N E C T I V I T YM A N A G E M E N T 51 .egion Coordinat ing A g e n t s 5

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3. IntelligentInformation DisseminationServer (IIDS)8IX .CON CL US I ON S 01 A .EVIEW OF R E S E A R C HQUESTIONS 01 B.E C O M M E N D A T I O N SFO R FUTURE R E S E A R C H05A PPE N D I XN TE L L I G E N CE E S TI M ATE 07

LIST OF R E F E R E N C E S 19

B I B L I OG R AP HY 21

INITIALDISTRIBUTION LIST 27

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X IV

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I . INTRODUCTION

A .REA OF RESEARCHR e s e a r c hin reconnaissanceh astraditionally focusedondatacollection,leadingto

n e w an dimproved m e t h o d sofdetectionan ddiscrimination.o w e v e r ,lessefforth asb e e n b e s t o w e din using information technology as anintegrated m e a n s t o transform th ecollected data into usefulinformationan ddeliveringit to ke yc o m m a n dan dcontroln o d e sin t imefo roperationaluse.h epurposeof thisthesisisto proposeam o d e lofintelligentsoftware agentstosupportth edecision processan dreconnaissance-relatedtasks.h em o d e lca n b eusedto assistwarfightersin managingday-to-dayactivitiesan dcrisisactionplanning.ke yobjectiveis to facilitatedataintegrationan dcoordination throug h in tel l igentagentsin a network-centr icmultisensorenv ironment .singintelligentsoftwareagents ,c o m m e r c i a l -off-the-shelfC O T S )echnology,n dariousetworkechnologies ,h eesearchwilldefineth earchitectureofaninformation system capableofcollectingdatafrom dispersed, heterogeneousatasources,processingandfusingthatdata,n dpresentingth eresul tantinformation to th edecision-makers.

B.E S E A R C H QU E ST IO N S 1 .h atare th e m a j o r characteristics of softwareagents?2 .h atreh eurrentechniquesorevelopingn deployingntell igentsoftwareagents?3.o w canintelligentsoftwareagentsb eusedtoassist/supportth ewarf ighterin

th edecision process?4.h atisanapplication ofanagentm o d e lfo rsupportingreconnaissancerelated to current decision processes?5.h atg o v e r n m e n tan dcommercia lprojectsar ebeingdevelopedusingsoftware

agents?

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6. W h a t issues ar e involved with a g e n t m a n a g e m e n t , m a i n t e n a n c e an d coordination?

C.EF INITIONS A N D S COP E O F R E S E A R C H T h etraditionaldefinitionofreconnaissancebringsto m i n daphysicalsurveil lance

ofanrea,suallyobtainntelligenceo rmilitaryse .econnaissancessetsre typicallyt h o u g h tto b ephysicalobjects ,suchaspeople,camerasorsatellites.h eoverallpurposeofthesessetsstoollectinformationa b o u tth ententn dapabilitiesofth eopposition,nd akeh eatavailableoh eecision-maker.d v a n c e m e n t sn technology,an dimprovements in communicat ions ,h a v ecreatedn e w m e t h o d sofgather ingdataandn e w typesofsensors.norderto e n c o m p a s sal lassetsavailableto providedata,th etraditionaldefinition ofreconnaissancem u s tb eexpandedb e y o n dth erealm ofphysicalsurveillance.h r o u g h o u th isesearch,econnaissancesefinedoeh eollection,analysisn disseminationofinformation.hetherhenformationsatheredysatellitesystem or acomputer program is immaterial .

T h eesearchil lnvestigateowntel l igentoftwaregentsanuppor treconnaissance-related tasks.h ed o m a i nof thisthesisisth ed e v e l o p m e n tofadecisionsupportm o d e lh a tsesntelligentoftwaregentsossistwitheconnaissancen d providedecision supportin a c o m m a n d andcontrols ys tem.h etechnology to createbasicintelligentoftwaregentsxistsnh e arketplace.heuthorsakeh eurrently availablee c h n o l o g yndroposee w ethodsfpplication,hichequireh edevelopmentofn ewapabilities.h ehesissonceptualnature.h ehesisls o providesr e c o m m e n d a t i o n sonw h e t h e randh o w intelligentagentscanb eusedto improve c o m m a n dndontrolystems,nduggest ionsorurtheresearch,ncludingh edevelopment of a wo rking prototype.

D.E S E A R C H M E T H O D O L O G Y A n extensiveliterature review w asconducted to examineth ebackground,concepts ,an dtechnologyofintelligentsoftwareagents . B o o k s ,periodicals,th eW o r l dW i d eW e b

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1.ndependence A na g e n th asth eability tooperatewithoutu s e ri n v o l v e m e n tonceth eu s e rh assent

th ea g e n to uto nit smission.heuser programsth ea g e n tw i t hit sparametersfo robtainingth edesired information thenal low sth eagentto w o r kautonomously untilcomplet ion .h e a g e n tdoesn o th a v etobeginexecutionimmediatelyafterth euserp r o g r a m sit .h ea g e n tm a yayo r m a n tntil pecifiedim ervent,h enctivatetselfan darryu tth ein tendedtask(s)independentof th euser .o rexample,ana g e n tm a ybep r o g r a m m e dto searchastockm a r k e tdatabaser ightbeforeclosing t imeeveryd ayto seew h i c hstocksare extremelyactivefo rth ed ayan dnotifyth euserthrough anemailm e s s a g e .h eu s e rdoesn o th a v eto si tath is system ev erydayatthist imeto checkth emarket .h ea g e n td o e sthisindependentof th euserandallows th e user to spend t i m edoing m o r eimportant things .2.earning L earningis th eability fo ranagentto modify it sbehaviorin responsetoachanging environment . On em e t h o dflearningsoeplicatese rctionsh e nxecutingparticular task.h is focuseson th epersonalassistantview ofanintell igentsoftwareagent .A h u m a n personalassistantlearns th epatternsan dtraitsof th eperson theyare assisting a n d incorporates thoseintoassisting thatperson.na g e n tdoesth es a m ethingin ac o m p u t e renvironment . Itw a t c h e sth euser'sactions,keepstrackof thoseactionsan dmodif iesit sbehavior to it s user preferences.

T h elearning usuallyoccursthroughobservation,userfeedbacko rtraining. [Ref .5]A sm e n t i o n e dabov e ,th eagentcanlearnthrough w a t c h i n gth euser'sactions.h e

userc a nmodifyth eagent 'sbehaviorb yprovidingfeedbackto th eg e n ttom p r o v ets performance.f th einformationthat th eagent returnsis consideredusefulin th econtext ofth esearch,then th eusercanprovidefeedbackto th ea g e n tto reinforce thistypeofsearch.P o o rsearchresultscanb ediscouragedthrough userfeedbackaswell.h eusercanalso train th ea g e n tbyrunningsimulationsofdifferentscenariosto buildak n o w l e d g eb a s efo rfuturese . Trainingng e n tor pecificy peofmissionwillm p r o v eh egent's

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performanceduringreal-worldexecutionofthatmission .seri n v o l v e m e n twilldecrease andw i l locusnth enformationreturnednsteadofmakingu m e r o u sef inementso reach th edesiredend state.

3.ooperation W h e nw ou m a n sooperateoccomplish ask,h eyooh r o u g h

c o m m u n i c a t i o nan danunderstanding oftheiro w nan dth eother'smission .r o g r a m m i n g cooperation b e t w e e nagentsishighlycomplex.ndependentcooperat ionrequiresd y n a m i cmodificationofcodetxecutiont imenrdertoreatenffectiveivisionoflabora m o n gh egentsnvolved.ierarchicalooperation ayeimplerheren coordinat ingg e n tssignsindividualtasksndcontrols ,oordinatesndynthesiz esh eresultsftheirollectivefforts.ommunicat ionsro tocolseededol lowh e softwareagents to comm unicate their informationan d w o r k jointly o n an unresolvedissue.

4 .easoningR e a s o n i n gisth eabilityto m a k eadecision.h eabilityofana g e n ttoreasono rm a k einferencesas to th ebest m e t h o d to accomplish it stask forces th ea g e n t p r o g r a m m e r to developanapproachw h e ndes igninganagent.herear ethreeapproachesto thisissue:rule-based,knowledge-based,andlearning.Ref.5]A rule-based approachrefersto giving th ea g e n tcertainrulesorparameterstofollowandisth eeasiestof th ethreeto program. K n o w l e d g e - b a s e dapproachesrequirenxperttoompilevastm o u n t sofinformation,w h i c h is subsequently given to th eagent to determineany particular behavioro r m e t h o d softh einformation.serorexpertinvolvementisextremelyhigh.earning,sm e n t i o n e dabove,takesth einformationthat th eagentacquires from th euser through repetitive tasking and feedback.h ea g e n tuses thisinformation to modify it s b e h a v i o rfo r future tasks.

5 .ntelligenceT h elevelofintelligencethatanagent possessesisin directcorrelation toth edegree

ofuseofindependence,learning,ooperationandreasoning.h em o r eofeachoftheseareasthatanagentuses,th em o r eintelligentanagent ' sfunctionalityisconsidered .sth e

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levelofanagent'sintelligence increases,t h o u g h ,th elevelofp r o g r a m m i n gdifficulty g o e supdramatically. g e n t sshould remain ass impleasneeded fo r their particular task.gentsthatequire i g h e revelfntelligencewillakem o r eh o u g h tn dffortnheirdevelopment .nasystem thatusesmultiplegents,th eegreeofintelligencenvolved withth egentswillaryasednth etaskofeachonendm u s tb ekeptsimplespossible to w o r k effectively.

G . S P E C T R U M O F S O F T W A R EA G E N T C H A R A C T E R I S T I C S A softwarea g e n tt a x o n o m yclassifiesagentsbasedo nw h e r ethey fallwithin seven

differentareas(Figure2 )[Ref .6].'h isspectrum helpsto identifyth echaracteristicsand abilitiesof agentsperforming various typesof tasks.

1.ntell igenceA nagent'slevelof intelligence ca n bet h o u g h tof in termsofw h e t h e r th ea g e n tonly executes imple,pecifiedaskrash ebilityoearnro mh ese rn dtsw n environment .h elevelsfo rthist a x o n o m yare preference,rigid,reasoning,planningand learning.

2.igidA rigida g e n tisb a s e donfixed,impleruleswithn oearningcapability.tonly

executespecificnstructions.h euserm u s tknow exactlyth enformationneededand,ideally,th esourceofthatinformation to accomplish it s task.

3.referencePreference is based solely o n evaluating decision criteria.h edecis ionvariablesar e

builtinto th ea g e n tb yth eauthoro r th euser.trequireslittleintelligence o n th epar tof th e agent .na g e n twith littleintelligence m ayh a v ea problem in determiningthattw o piecesof information are th es ame.

1ortionsof th efollowingsectionar etakenverbatimfromDr.Bui'sarticle,thisrequest,nordertonsurecompletenessin th e technical report.

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SoftwareA g e n tT a x o n o m y Intelligence Rigidreference Reasoning Planning LearningMobility Stationary MobileTemporal Adhoc Cloning PersistentInteraction Agent-Agent Agent -Application Agent -UserTask

Specific GeneralEnvironment

Stable Stochastic

Behavior Aut o n o myCo Ua b o ra t io n Co o pe ra t io n Co mpe t i t ive Cha mp elayC r e w

Figure2 .SoftwareAgentTaxonomyFrom R ef .[6 ]

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9.nteraction A g e n t sh a v eth ebilitytonteractwitheachother,withvariousapplicationsan d

users.Ana g e n tthatw o r k swithotheragentsm ayw o r kin a peer-to-peerarrangemento rahierarchicalone.h iscouldrequirespecificcoordinating,facilitating ormitigating agents .A na g e n tcanw o r k with applicationssuchasdatabases,w ebbrowsers ,spreadsheets,etc.c o m m o nnterfacea n g u a g esssentialoh isy pefgentecausefh eariousoperatingystemsn dnterfaces.sersanw o r kwithgentsh r o u g hraphicalserinterfacesor through th e p r o g r a m m i n gof th eagents .

10.asksA n agent'staskischaracterized as specificor general. na g e n twithaspecifictask

isoptimallydesigned fo r thato netaskonly.na g e n t witha generaltaskisasuperagent,a jack-of-all-trades.tm a yeoeneralh a ttso tb leoin d pecificieceofinformation,b utm i g h tdiscoverinformationthatth epecifictaska g e n tmissedduetoit slimited scope.

T w oother typesof taskagentsinclude information-specifica n d task-specific.Ref .9]Annformation-specificg e n tsr o g r a m m e doealwithnly pecificy pefinformation .heseagentsknow w h e r eto locateparticularinformationin diversenetworks ordatabases. task-specifica g e n tisdesignedto accomplishit smission regardlessofth e typeofinformation requested.tc a ncoordinate withotheragents,if requiredto achieveit sgoal.

Front-endgentsn dack-endgentseferow oypesoftaskingorgents .Front-endagentsdirectlyinteractwithth euser.h euserinterfaceswiththeseagentsn real-time,requesting informationo rprovidingguidanceanddirection fo rth eagent.ack-endagentssupportth euserb utdon o tdirectlyinteractwith h i m .hesesupportingagentsdoth ebehind- the-scenestasksrequired b yth euser'ssystem suchas periodicupdatesan d coordination.h ese rhouldo teedopendim eirectingroordinatinghese agents .

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1 1 .nvironments During it sT T L ,th eenvironment thatth ea g e n tl ivesin iseitherstableorstochastic.

A stablee n v i r o n m e n t doesno tchange .t isa secureenvironment . n a g e n tthat w o r k sin astablen v i r o n m e n tism o r eikelyorovideurrent,ccurateatawithittleh a n c eofprovidingw r o n ginformation.ittlechanceofattack,virusinfectiono rinterception exists fo ra na g e n tin thisparticularenvironment .stochastice n v i r o n m e n tfo rana g e n tisn insecurenvironment .om erobabilityfrandomnessn dncertaintyxists.his e n v i r o n m e n touldequiredditionalkillsndn o w l e d g eyh euthorh e n p r o g r a m m i n g th eagents.n thisenvironmentam u c hh i g h e rriskofattack,virusinfection,interception plagues th eagent.

12.ehaviorAna g e n tcanb e h a v ein m a n ydifferentw a y sdepending ontheirtask,intell igence

andagency. a .utonomyThisa g e n t w o r k so n it so w n .f an a g e n t w ork so nit so w n then it potentially

doesn o th a v eto w o r r ya b o u tcollaboration,cooperationorcompetition.h einformat ionitpresents is a one-sided view of th einformation collected.

b .ollaboration Thisa g e n t w o r k swithotheragentsto solvea problem orcompletea task.th asth eenef i tofadditionalourcestoalidatetsnformation.h em o r eourcesh a tconfirm th es a m einformation,th ehigherth elikelihoodthatth einformationisvalida n d correct.

c .ooperativeThisg e n tssiststheragentschieveheirmission .tw o u l do tse d primarily to g et information, bu tis optimized to helpother agents to w o r k effectively.

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d .ompetitiveAompetitiveg e n teeksoptimizetselfv enth expensef

degrading th e performanceof otheragents.e.hampionA c h a m p i o nseeksto w in n omatterth eoutcome.h is a g e n td o e sn o tcarew h a tth a soooompletetsmission.tplacestselfatth eopofth emportance hierarchy. /elayA relayagenth a n d sofftoanotheragentw h e nfinishedw i t hit sportionof

th etask.tpasses .stateandinformation to another agent(s)to complete th efinal task.g.rewsC r e w sfgentso rkimultaneously it hachther.h isequirescoordinat ion a m o n g agents .

H . IS S UES O F M O B I L E A N D DIS TRIB UTEDA G E N T SIntelligent softwareagentscan beof tw otypes:statico r mobile .taticagentsreside

o nonecomputersystem an dneverleavethats ys tem.obileagentsh a v eth ebilityto leaveth esystem from w h i c htheyoriginatedandm o v eto anothersystem o rpossiblym a n y differentystems.singmobilegentsnsteadofstaticgentsresents u m b e rofdifferentissues.[Ref.6 ]

1.rogramming A mobile a g e n tshouldbep r o g r a m m e din a language that allowsth eexecutingcode

toaltxecution,reservingtaten dounter,ndm o v eo ifferentocat ionn d continuerunning.tm u s talsoecapableofrunningo navarietyofsystems.ultiple operatingystemso se ignificanthallengenr o g r a m m i n gng e n tn a n g u a g eunderstandable b yal l possibleoperatingsystems.

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2 .afety Ifn o tontrolled,ng e n touldotentiallyauseeriousa m a g eo ystem.

Protectivem e a s u r e san dcontrolsare requiredto preventthisro m happening.na g e n tm u s t n o tobtainaccessb e y o n dw h a t th eh o s tsystem allowsfo r that particularagent .n thisrespect,th e h o s t is th evulnerable partyand requires protection.

3.esource usage A na g e n th asth epotentialto monopoliz e th eprocessor,harddrivean dm e m o r y ofth eh o s ts ys tem,ifn o tcontrolled.o w e v e r ,th ea g e n tpotentiallyn e e d ss o m eo rallofth e resourcesof th eystemtoxecuteit stask.h eystemshouldm a k ellocationso rth e agentto execute,b utn o ttakeover,th esystem resourcesin am a n n e rthatunfairlypreventsanythingls efrom usingthoseresources.nequitableresourcel lowancefo rbothth eagent to accomplish it s task andfo r th esystem to cont inuefunctioning is required.

4 .avigation In networkednvironment ,avigat ionpresentsm o r eofahallengehannsingles ys tem.avigat ingto th er ightlocation to obtain th edesired information iscriticalfo rh egent .ng e n tm u s tnderstandh eath(s)equiredoeachh entended location(s).n addition,th ea g e n tn e e d sth epath o rm e a n sto sendo rprovideth eretrieved information back to th euser.

5.rivacyA mobilea g e n ttravelstonothersystem withit surrentstatendp r o g r a m m i n g codetoexecutedtobtainth eesirednformation .henformat ioninsideth eg e n tneedsprotectionfrom outsidesources.neschooloft h o u g h tis thatallstateinformation shouldbehiddenfromth ehost.h ereceivingystem h asn on e e dtoeenyra w datacarriedb yth eagentorstateinformation.notherviewpointis thatagentsm u s tbeableto verify theirstateinformationandm a k enecessarymodif icat ionsto datacarried within them [Ref.10].

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A n o t h e rissueis thatth einformationwithinth eh o s tsystem n e e d sguardingagainstunauthoriz eddisclosure.hehostsystem protectsitself byonlyrevealing informationto th eg e n th atsl low ed.h eystemsh ath eg e n tisitsm u s terotectedro m unauthoriz eddisclosure to th eagent .

6.ommunicationA form ofc o m m u n i c a t i o nis n e e d e db e t w e e nusersan dagentsandpossibly a m o n g agents .n a g e n tthatretrievesth edesired information needsto c o m m u n i c a t ethatb a c kto th euser in s o m e format. n agent thatcannotfind th einformationh asto inform th euser ofthatas well. asedon th e p r o g r a m m i n glanguage,th ec o m m u n i c a t i o nm e d i u m has toallow fo ru m e r o u sypesfnteractione t w e e nh eg e n tn dheser.g e n t s a y c o m m u n i c a t ewitheachother dependingo n their task.haring informationb e t w e e nagentsrequireso m m u n i c a t i o nro tocoloffectivelyo rko g e t h e rn ulti-agentenvironment .

7 .ontrolA g e n t scannotru nautonomously withouts o m eform ofcontrolsplaced upon t h e m .

C ontrolsc an rangefrom resource usagelimitations,to authority to entera particularsystem,toim el lowedtoiv en dxecuten cen ys tem.h ehallengerisesw h e nth econtrolsplaced o n th ea g e n tb yth euser /programmer conflictwithcontrolsplaced uponth e agent by th es ys tem.T h echallenge today isto figure o uth o w to besti m p l e m e n tagentsso they boost productivityrather thancreatechaos ."Ref .7 ]

I.E C H N I Q U E SO R L A U N C H I N G I N T E L L I G E N T S O F T W A R E A G E N T ST h e r erethreetechniquesse dto passequestsb e t w e e nangent , usera n d

serverrost.heyree m o t erocedurealls,e m o t er o g r a m m i n gn dsing middleware .

1 .ynchronouscommunicat ion-oriented remoteprocedurecall( R P C ) . remote procedurecallisa traditionalprocedurecallk n o w nasa requestan d replycycle.T h eusersendso u tarequestfo ranothersystem to c o n d u c taprocedure. O nce21

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th eh o s tsystem completesth eprocedure,it sendsareplyb a c kto th euserwith th erequested information.h is is t h e m o s tc o m m o n of the three types.

2 .s y n c h r o n o u smessage-orientedagents .h emessaging approachpermitsth edistribution ofdataorcontrolthroughth euseofmes s ages .h is isalsok n o w n asr e m o t ep r o g r a m m i n g(R P). essage-oriented ism o r eflexiblean dd y n a m i c thanR P C .tdoesn o trequireaconstantdirectconnectionb e t w e e nclientand server.Pcan beaccomplished using email.

3.ntermediar iesratabasemiddleware .atabasem i d d l e w a r es oftwarelayerh a trovidesransparentccessoo m o g e n e o u sn deterogeneous relationalrtheratabasescrossultiplero tocolnvironments . M i d d l e w a r esoncernedwithprovidingth egentaccessatherthanpassingm e s s a g e s b e t w e e n agentso r with aserver or host.

J. T E X T R E T R I E V A L A N D D O C U M E N T M A N A G E M E N T IS S UESC u r r e n to m p u t e rystemsa v e illionsndillionsfo c u m e n t sndiles

associated w i t ht h e m .e v e rin historyh a v esom a n ydocumentsexisted.ettingto th e informat ionn e e d e drequiresm o d e r ntools,uchasgents,oin dexactlyw h a tth eu s e rw a n t sn deeds .a tailteringnda tausiono thelph eseretloseroh einformat iontheysoesperatelyneed.earchengineshavehelpedwiththisprocess,utmostoo tl lowh ese romodifyh emnuch m a n n e rsoearnh eser'spreferences an d desires.

1.ata Filtering D a t afilteringsh eprocessofsiftingh r o u g hv o l u m e sofdatatoetermineth e exactinformat ionbeing sought.W i t h th eh u g ea m o u n tofdata readilyavailable o ntoday'sc o m p u t e rsystems,th eabilitytoif tthroughitquicklyandefficientlyiscrucial.earche n g i n e su s e do nth eInternet,suchasInfoseekan dExcite,usedatafiltering toreturnth eresultsofth esearch to th euser.

Searchnginesurrentlynth emarketo decentjobofreturningn s w e r so searchqueriesfrom largea m o u n t sofdatain at imelymanner .e y w o r dsearchesar eth e m o s tprevalenttypesused.om eearchenginesusea g e n ttechnologytossistthemn their tasks.M o s tsearch resultsonly h a v ea fe w relevant hitsconcerning th einformation th e

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III.B A C K G R O U N D A N D C O N C E P T S A ng e n tnablednformationys tem,askedoupportntelligenceathering,

reconnaissancen dperationallanning,ouldonsistfnrganiz at ionfc o m m u n i c a t i n gsoftwareagentsw h i c hgather,process,an ddistilldataan dinformationo n behalfofth euser.

T h eadvantagesto suchasystem are profound andfar-reaching.h eshearquantity ofdataavailableto h u m a nusersm a k e sth egatheringofinformationa l m o s timpossibleto accomplishexhaustively.gentsanhelpyatheringataanddistillingt,e m o v i n g redundant ,rrrelevantata,ndynthesizingtn to eportrisplay se ran understanduickly.heseystemsaneuiltounnside ser'sW ebrowser ,integratingal lofth eadvantagesofH T M L .rowsertechnologyal low sfo rth edisplayoftext,graphics ,an d mult imedia files.

Inrderonderstandh ew ayng e n tasednformationystemworks,tsimportantonderstandom eeyonceptsnermsfheirpplicationog e n tp r o g r a m m i n g .heseconceptsare largely basedonG eneralM a g i c ' sw o r k in th efield[Ref .4 ],asw e l lasotherresearchers referenced in ChapterII .h eyare presented andexpanded hereoo rm r a m efreferenceoronceptsresentedh r o u g h o u th ishesis .h e G e n e r a lM a g i cparadigm isreferredto in thisthesisbecauseit smobilea g e n ttechnology m o s tclosely fulfills the requirementsof this project.

A . R E M O T E P R O G R A M M I N G M o b i l egentso rkn onceptalledemoter o g r a m m i n gR P )here

computer- to-computernteractionsccomplishedb yotonlyallingroceduresnth eremotecomputer ,asin aremote procedurecall,b utbyalsosupplyingth eprocedureto b e called.ac h a g e n ttransported byth en e t w o r kincludesaprocedure to b eexecutedo nth ehostm a c h i n e ,an ddatathatar eitsarguments .hereforeaclientandaservercaninteract w i t h o u tn g o i n gommunicat ion . T h erchitectureapsloselyoh eradit ional

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client/serverarchitecture,b utin thiscaseth e"client"isth euser'slocalc o m p u t e rsystem,and th e"server"isany r e m o t esystem thatprovidesmobilea g e n tservices.

T h eerformancem p r o v e m e n tofremoter o g r a m m i n gv e rraditionale t w o r kc o m m u n i c a t i o n sependsnh eetwork.ts o red v a n t a g e o u sose e m o t ep r o g r a m m i n gparadigm onl o w e rb a n d w i d t hnetworkslikeawirelessL A Natsea,thanin highb a n d w i d t hfiberopticetworks.clientn e e dn o tb econnectedontinuouslytoremoteserverin orderfo rth euser'sa g e n tto gatherinformation,storingitfo rt ranspor tto th euser'scomputerth enextt imeheorsh elogso n.h euser'sc o m p u t e rd o e sn o tn e e d to beconnected w hile thea g e n t carriesout it sassignment .

1 .obile AgentsAm o b i l eoftw areg e n tsapablefinterruptingtsxecutionfcertainse rdefinedcondit ionsar emet,avingitscurrentdataan dstateinformation,a n ddirectingit s

o w nmigrat iontoanothersystem orplace.ncein th en e w place,th eagent'sexecution pickspw h e r eteftff.ntherw o r d s ,h executionofamobileg e n tppears continuouseven if it migratesfrom onec o m p u t e rsystem to another .

2.laceIn mobilea g e n ttechnology,an e t w o r kofcomputersisacollectionofplaces.

placeoffersa service to th e mobileagents that enter it.h e placeisreallyastationarya g e n tdesignedoommunicateirectlywithmobilegents ,n drovide rotectedre an m e m o r yfo rth eagentto execute.gents,including stationaryagentsoperatingasaplace,runwithinavirtualmachine,nddon o tdirectlyaccessth eh a r d w a r e ,peripherals,orfile systemsofth eh o s tcomputer .hisisprimarilytoreventviruses,u titls onsulatessystemsfrom poorly p r o g r a m m e d or o therwisemaliciousagents.

InGeneralMagic'sO dysseyp r o g r a m m i n gnvironment ,o rxample,h egents ru n within th e J a v a virtualm a c h i n e ,andare therefore r e m o v e d from th eoperating system orh a r d w a r elevels ,enhancingth esecurityandstabilityofth esystem asaw h o l e . furtherbenefitofth eabstractionisthat,likeotherJ a v aapplications,a n a g e n tcanru no ndifferentplatforms.

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E a c hplacesr o g r a m m e dtorovide pecializedervice.heseervicesan includeil eervices,nterfaceswithocal,hirdartyearchpplications,rataase m a n a g e m e n tsystems.hus,th ep r o g r a m m e rofamobilesoftwarea g e n tdoesn o tn e e dto k n o w th eoperatingsystem,filestructure,o rdatabases c h e m aofth eserverswithw h i c h th e a g e n twillinteract.eorsh esimply programsth ea g e n tto interactwithas tandardplace,an dth ep r o g r a m m e rof th eplaceprovidesth edatabaseo rfileervices,whileth evirtualmachine providesan interface with th eoperating s ys tem.

A serverm a yofferaplacetog etw e a t h e rdata,notherplaceto obtainth eatestintelligencedata,an dathirdfo rgeographicinformationfo rtarget ing. hosta g e n tatth e"IntelPlace",fo rexample,m i g h tcheckau s e ragent'scredentials,securityclearance,an d access,thenprocessit squery,earchth eatabase,ndreturnth erequestedinformation .U nauthoriz edagentsw o u l dberefusedan d"dest royed,"o rdeleted from m e m o r y .h eh o s ta g e n talsok n o w sb o u tsimilarh o s tagents,ndcandirectth emobileagentto them fo radditionalinformat ion.

T h eH o m ePlaceso naclientsystem ar eactuallystationaryagentsthatserveasth e pointsof departureandreturn fo r agents that th e user sends to remote places.

Figure 3.Agent Places

TravelAnagentca ntravelfromon eh o s ttonotherwhiletisrunning,mamteiningts

procedurendtate.fth etripucceeds,h egent'se xtnstructionsxecutedtts destination.hus,in effect,networkingis reduced to asingleinstruction.

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4 .icketA t icketisan e t w o r kaddress,oralistofnetworkaddresses,tow h i c ha na g e n tis

authorized to travel.t mayalsospecifyth en e t w o r kth ea g e n tm u s tuse,restrictingtravelto th eS I P R N E Tonly ,fo rexample.h eaddresseso nit st icketcorrespond to th esystemson w h i c h th ea g e n t(user organiz at ion)h asan account.

Ifaserverplacek n o w sofanotherplacethatm i g h th a v ebetterinformationitcan s u g g e s tthatplaceto th eagent .fth eagenth asa n accounto nth esuggestedsystem,itcan requestpermission from it suser to c h a n g eit sticket.f th esuggest ionc o m e sfrom a trusted h o s t th ec h a n g em a yhappen w i t h o u th u m a n intervention. therwise,aservercancreatean a g e n tofit so w n to retrieveth erequested information, andleavebehind arequestto addan a c c o u n tfo rth enewagent .uturesearcheswillg odirectly to th en e w place,reducingth e work loado n th esystem asawhole .h eagentlearns.lternatively,th ea g e n tcanreturn h o m ea n dinformit suserofth en e w place.h eusercanrequestthatana c c o u n tfo rh is a g e n tb ecreatedo n that m a c h i n e a n d m a k e th ec h a n g e to th e agent's t ickethimself.

5 .gent AccountsA ng e n tc c o u n tseryimilaronccountstablishedor se rnny

network,l lowingh eg e n too g o noh ee m o t eerver.h eccountdentifiesh eauthorityth ea g e n trepresents.M o b i l eagentsm u s tlogin justash u m a n usersm u s tlogin,thereforea g e n taccountsmustbeestablished in advanceo nsystemsto w h i c hauserm i g h tw a n ttosendhis/heragents .nup-to-datea c c o u n tlistallowsth eagentsto visitallsitesnecessaryto retrieveth enformat iontoa n s w e rqueries.sn ew informationsourcesar ediscovered,g e n tccountsm u s tb establishedwiththoseources,ndh ec c o u n tis tupdated. g e n t scandiscover n ew sourcesthrough redirection byserverstheyh a v evisited,orbyuserinput.Asystem thatdoesn o t h a v eanupdatedaccountlisting restricts th eplaces w h e r eagentscang o to thoseoriginally programmed.

T h e r eare var iousw a y sto m a n a g eth ea g e n taccountlisting.n em e t h o drequiresah u m a n system administ ratorto establish accountswith eachsitethatth em o b i l eagentsm ay

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m a yn e e dto c o m m u n i c a t ewithit sh u m a nuser.h eagentthatg o e sin searchofw e a t h e rinformat ionm i g h tsendto anagentath o m easmall-scalem ap.h ea g e n tath o m ew o u l d present thatm a p to th euser v ia a graphicaluser interfaceandallow th euser to specify m orepreciselyth earea h eisinterestedin b ydrawinga b o xarounditwith h is m o u s e .h eh o m ea g e n to m m u n i c a t e sh a tbacktoh ee m o t eg e n tw h oathersh eppropriatew e a t h e rreportan d returns.

8.uthoritiesT h eauthorityofanagentorplacecorrespondstoth eindividualororganiz at ionin

th ephysicalworldthatth ea g e n trepresents .hecommunicat ionsan dsecurityprotocols(presentedin ChapterIV )ensurethatagentsandplacescanreliablydetermineeachother'sidentity.h eprotocolrequiresth everificationofth euthorityofanagentacht imettravelsro mnelaceonother.laceserifyh euthorityofotherplacesrioro transferringana g e n tto t h e m . e w placesverifythatanagentdid in factfollow th epath it reportsasw e l lasverifying th eauthorityofth eagentsitself.nm o s tcases,cryptographicf o r m sofproof are required.

T h erocesssnalogouso sero g g i n gno trustedys tem.h eg e n tauthenticatestself to th eplace,ndth eplaceauthenticatesi tselfto th eagent .g e n to rplaceidentificationsan dpasswordsare exchangedandcomparedwith th elistofauthorities w i t hw h i c hh eg e n trlacem ayommunicate .ac kofanonymityls ol lowsorauditing ofa g e n torserveractivity.serscanknow with confidence w h e r etheira g e n th asb e e n w h e n itreturnsan danyfilesitcarriescanbes tampedwith th eoriginator 'sauthority.Likewise,erversantrackw h a tgentsh a v eeenervicedan dhargeccordingly.fhostileo ru n k n o w nagentsh a v eat tempted to penetrateth esystem,thateventcanb el o g g e das w ell .

9.egion A regionisacollectionofplacesprovidedb ycomputersoperatedu n d e rth es a m e

authority.suallyh ee g i o nw o u l deh eo m p u t e rys temsfaargerganiz at ionmaintained b y th esame InformationSystemsD epartment .

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T h i sn e w a g e n tcanleavearequestthatana c c o u n tb eestablished o nth en e w server.

ermissionsoExecuteCertainnstructions.ermissionsrerantedo executecertainfunctionalitiesthatare eitherp r o g r a m m e dintoth ea g e n torar elearned b yth eagent.e m o t eplacescouldsupplyproceduresfo rth ea g e n tto execute,o r thatar eexecuted on behalfof th eagent.xamples m i g h t b ecloning,orextending th eagent'slistoft rusteddomains .h ea g e n tm u s t then h a v ease toftheseinstructions thatitisal lowed to execute.h elistm i g h talsob eregionspecific.ertaininstructionscanbeexecutedin certainregions ,rb ycertain regions.gain ,fsecuritys oncern(andtl w a y ss)n ea n n o tllow agentsto h a v e their listof trustedd o m a i n sextended b y justany place.

uthoritieswithWhichtheAgentM ayInteract.Ana g e n tm u s tcarrywith it ,in addition toit slistoftrustedd o m a i n sandn e t w o r kaddresses,th eelectronics ignatures(encrypted)ofallof th eAuthori t ieswithw h i c hitm ayinteract.meeting alwaysbeginswith achallengean dreplyroutine.a cha g e n to rplacemustdentifytselfoh eatisfactionfh ethereforen ythercommunicat ion canoccur. lternatively,th elistof electronic s ignaturesm a y b e k eptth eo m elacen duthentication ayappenystablishingconnect ion b e t w e e n th e mobilea g e n t andit sh o m e place.

B. T H E Q U E R YF O R M A T O neof th eadvantagesofmobileagentsisthattheycanb ep r o g r a m m e dto m a k e

queriesw i t h o u tk n o w i n gth estructureofth edatasourcestheym i g h tvisit.a chplaceis p r o g r a m m e d to acceptqueriesin a"standarda g e n tformat"an dreformatthem to w h a t e v e r th elegacydatasourceresidingo nthatm a c h i n eisexpecting.herefore,ana g e n twitha singlenaturall a n g u a g equerycan requestinformation from a database,asearchengine,oranewsgroup.h eplaceisp r o g r a m m e dto acceptth equery,translateitasappropriate,and passito nto th eapplicationrunningo nth eerver,w h e t h e rit's databasem a n a g e m e n ts ys tem,a thirdparty search applicationlikeVerity'sSearch9 7,o r justas implefileofw o r d processordocuments .

A ts o m elevelth equeryformatm u s tb estandardized andstructured.v e nif itis n otnecessaryfo ran a g e n tto beableto c o m m u n i c a t edirectlywith ar e m o t edatabaseo rfilesystem becausethatfunctionality isprovidedb yth eplace,itisstillnecessary fo rth ea g e n t

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tob eableto c o m m u n i c a t ereliablyw i t hth eplacein w h i c hitisrunning. S o m eformofstandard ontologyis required.

C. A G E N T E N V I R O N M E N T A ng e n tasednformat ionystemshysicallyoifferentro mn yther

networkedinformationcenter.tisaclient/serverarchitecturew h e r eagentsrepresenting usersare freeto m o v efrom localclientmachinesto remoteservers,executetheircodean d eitherreturn,m o v eto anotherserver,orsendinformation backthrough aconnect ion.hea g e n tm ayeitherd ie in place,orb e c o m edormant ,"living"in th ehostserver 'sm e m o r y orfilesystem,wait ingfo rpresetcriteriatobem et .h ecriteriaarelimitedbyth eagent 'spermit .

A softwarea g e n treallyonlyexistsasanagentwithinth eC P Uorm e m o r yof it sh o s tcomputer .hereforea g e n ttechnologyiscompatiblewithal lnetworktechnologies. W h e t h e rth enetworkprotocolisEthernet,T C P / I P ,tokenr ing ,etc.,th eagentistransportedo v e rth en e t w o r kin packetsjustlikeanyotherdata,an disreassembledin th ereceiving computer .n aT C P / I P network,th edatagramsarelabeled with th eportn u m b e rassociatedwith th ea g e n tplace.W h e n packetsarrivetheyareassembledin th eplacean dth ea g e n tis run.urther,foftwaregentsn dlacesrer o g r a m m e dnav a ,h enh eyre compatib lewith virtuallyallc o m p u t e rsystemscurrentlyin use.

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m u s tno tinterferewith th eefficientexecutionofotherprocesses.vailabilityalsorefers to th egraceful,reliable recovery of th esystem and data in caseofacrash.

6. Authenticity Inetworkedys tems ,uthenticityrovides eansfnsuringhatnly authorized usersh a v eaccessto th esystem.h esystem verifiesth eorigin o rdestination of

data b y recordingw h osentor requestedit ,ensuring thatth esubjecth asth eproperauthority toaccessth edata,andrecording th et imeanddatethatth eaccessoccurred.h isism o s toftenandledh r o u g hdentificationnduthentication.serdentifiesimselftoserverandauthenticates h is identity withs o m eform of proof.oensureth esecurityof th esession th eserverm u s talsoidentifyandauthenticateitselfto th euser.h is ismostoften handledh r o u g hsera m e sndasswords ,u thererearm o r eecurerotocolsavailable thatcan n o t onlyensure th e authenticity of th e user and server,b utcan alsoprotectth eintegrityandsecrecyof th einformation passedin th esession.h etw om o s tc o m m o n l y usedro tocolso recureetworkommunicat ionsreecureocketsa y e rSSL ),c o m m o nin internetcommunicat ions ,andthirdpartycertificates likeVerisign'sproprietary protocolfor authentication and encryption.

a. Secure SocketsLayerSecureocketsayersh em o s twidelysednternetecurityrotocol.

W h e naW ebb r o w s e rfirstconnectstoasecureW ebserver,th eW ebserversendsahellorequestto th ebrow s er .h ebrowserrespondswithaclienthellothatcontainsan u m b e rcalledasession ID thatuniquely identifies th ecurrentsession.h eclient helloalsotells th e serverw h i c hryptographiclgorithms,ompressionechnologies ,n dS Lersionh e b r o w s e rsupports.inally,itincludesarandom n u m b e rthatth eb r o w s e rgenerates.h eserver il lespond it h erverelloh a tncludesh eelectedompressionndcryptographiclgorithmro mh erowser 'sist,h eppropriateS Lersion,n o t h e rrandom n u m b e r ,andanacceptablesessionID .h isfirstsetofc o m m u n i c a t i o n siscalled th ehandshake,n ditstablishesth eprotocolsthatw illesedthroughth ee stofth e

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th emobilecodeors o m eother program withw h i c h itcommunicates couldh a v ea b ug in it scryptography m odule).4.nauthor ized accessto th eorganizat ion'sn e t w o r k resourcesandnodes .M o b i l e

c o d e ,n ceunningn erver,m ayttemptoccessrivatentranetsrn e t w o r k swithin th eorganization. b . Addressing th e threat

M o s tofthese threatsfallinto th ecategoriesofsloppya g e n tp r o g r a m m i n go rmaliciousoftware .sment ionedreviously,uthenticationsmportantoro thh e serveran dth elient,w h i c hin thisases m o b i l eagentexecutingtsod ewithinth e server'svirtualm a c h i n e .ortunately th eauthentication ofsoftwareagentsto th eserveris reallyn odifferentfrom authenticating aremoteuserto th eserver .h eu se ofcertificates,h a s h e dc h e c k s u m s ,n dcryptographyalllendthemselvesto th ereliableauthenticat ionofsoftwaregents .nceng e n tsroperlyuthenticated,ndtsntegrityverified,h e threatof malicious subversion issignificantly reduced.erverscan protect t h e m s e l v e sfrom sloppy p r o g r a m m i n g through th euseof allowancesand permits.

2.hreatsto Agentsa .otentialthreatB e c a u s eng e n teliesn o stomputero rtsxecution,malicious

serversa v ethebilitytolterth eataorodeontainednanagent .sersm u s te assuredthat theiragentsw e r en otc o m p r o m i s e dwhilevisitingaseriesof places.nanother form ofth edenialofserviceattack,n e t w o r kdevicescanbep r o g r a m m e dto w a t c hfo rdata packets from orto certain serversan d intercept,redirect,or destroy t h e m .

b .ddressing th eThreatT h eo b v i o u scounterto thesesecuritythreatsist h r o u g hauthentication an d

encryption .Butw i t hm o b i l ecodethatrelieso nah o s tcomputerfo rexecution,th eissueofauthent icat ionsroblematic .h eundamentalealityofmobileo desh a t mobile softwarea g e n tisnots o m ea u t o n o m o u sentitythatcan travelth einfosphereindependently .In allcases,th ea g e n tmustrunin th em e m o r yandcentralprocessingunitof ac o m p u t e r

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4.riorto departingPlaceA fo rB ,th ea g e n th a s h e sth eidentificationofPlaceB andadds that entry to it s travellog.

5.nroutetoB ,th ea g e n tisinterceptedandredirected to hostileerverZ.reportscorrectly that th ea g e n tc a m efrom A an d then causes theagent to believethat it is n o w on PlaceB .h eagentandanyinformationitiscarrying h a v en o w beencompromised,b utneitherth eagent ,n o rit suserw o u l d realizethatatthis t ime. tthispoint,Zm ayintroducemisinformat ionto th ea g e n torappendaTrojan Horseandsend th ea g e n tH o m e .h eagent logsthat it is returning to H.

6 .establishesaconnectionto Handforwardsth ecorrupted a g e n th o m e .W h e n th ea g e n tarriveso nH,trequeststh eaddressofth ePlacefromw h i c hith astraveled,as hestndcomparesttoh eddresstl o g g e dwhiletillnA ,H(B).h eresultwilln o tm a t c handth eu s e rwillknow thath is a g e n th asb e e n compromisedndxecution il lemmediatelytopped,reventingny furtherdamage .

N o t eh ath isystemeliesnh entegrityfth eertificatesassede t w e e nserversin th eS S Lprotocolto ensurethatserverscannotmisrepresentthemselves to each other.h isseasonableecauseh eerificationfth eertificatesappensnw o independentprocessors,insteadof in one processor,asis th ecaseofan agentauthenticating th eserver on w h i c h i t iscurrentlyexecuting.

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V .A F R A M E W O R KF O R AGENT- B AS ED DECIS ION SUPPORT:H E M OB DL EA G E N T R E C O N N A I S S A N C E KIT ( M A R K)

In it sinitialconcept,th eproposed system supportsth edecision-maker by providing reliableinformat ionto aid in criticaldecision making.ntelligentsoftwareagentsaren o tin tendedto replaceh u m a n sin th eecisionprocess.h eM A R Kystemisntendedto supportth eh u m a nuser.Itisalsointendedtow o r kw i t hexistings ys temstoeduceth e decision loopb yprovidingaccurate,detailedinformationin a t imelymanner .M A R Kacts asanintelligentpersonalassistanttoth euser,bydoingth etasksdescribedin C h a p t e rII.T h eintelligentsoftwareagentscansupport theh u m a n bydoingtasksthatar erepetitiveand requiresearchingthroughvastamountsofdata.M A R Kcandocomputationallyintensive tasksinvolving multiplevariables fasterthana h u m a ncan.M A R Kcandodatacomparison a n din tegrat ionto assist th eh u m a n user to developacomprehensivepictureofth esituation. C o m p u t e r sare m o r eefficientan dfasteratfiltering through v o l u m e sofdataan didentifying possibletrendsthanh u m a n sare.h eyar ealsolesslikelytodiscardapieceofdata justbecausetdoesotitapreconceivednotion.neofth egoalsofintelligentoftware agentssoighl ightptionsh a th eu m a nm i g h tav everlookedrismissedsins ignif icantecausee/sheidoteeh eause-effectelationshiph a th atption presents.

H u m a n sgenerallypreferto m a k eth eultimatedecision w h e n thatdecisioninvolvesh u m a nivesecauseewu m a n srewillingorust omputerom a k eh ebestdecision".ntell igent software agentscan makedecisionson w h i c h airline ticket to buy,fo rinstance,ecausetnvolves traightomparisonfmeasurableariables.utn intelligentsoftwarea g e n tshouldn o treplacea h u m a n regarding h u m a nlifedecisions.tis difficultto providecomputerswith intuitionan dprogram them to m a k evaluejudgements . Intelligentsoftwareagentsdon o tconsidersuch thingsas fear,desperation,orgreed.hese thingsauseh u m a n stoeactin w a y sthatm ayb eontradictorytoth e"logicalction"

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0

Figure4.M o b i l e AgentReconnaissanceKit

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agentsorm e s s a g e stheyendback,ecryptsndverifiesheirintegrity;,n dpassesn y information theycarryto th eappropriateL R E fo r processing.

2 . T he Server O nserverm a c h i n e s ,eachplaceverifiesth eauthorityof,andsetspermitsfor,each

ofth eagentsthatenterit.fana g e n tcannotproveit sidentityto th eplace'ssatisfaction, accessisrefused andth emobilea g e n thandlesth eexceptionb yreturning to it shomean d reporting thataccessw asrefused.incean agentcarries th eauthority of it suser,e a c ha g e n tthatvisitsth eserver 's placem u s th a v eanaccountatthatplace.h esystem administ ratorsetsu paccounts .na c c o u n twillh a v eallnecessarysecurityinformationto controlagents that arrivein a place,verifyauthorities,andse tpermissions. slongas ana g e n tis running o naserver,itneverleavesthe place.h atis ,itrunsinsideth eJ a v avirtualm a c h i n ea n d c a n n o taccessh a r d w a r eo rfilesdirectly. llservicesare providedb yth eplacet h r o u g hth e J a v avirtualmachine.h isetupl lowsor ecureperatingnvironment ,n delpsensurethat poorly written orhostileagentscannotcaused a m a g eto th eserver,o r viewfilestheyoo th a v epermissiontoee.lso,incellervicesreprovidedb yth elace,agentsn e e dn o t k n o w anything a b o u tth estructureofth edatabaseo r filesystem o n theh o s tserver. slong as th ea g e n tand the placecan communicate ,th edata can b e retrieved.

O nceanagent'scredentialsar everified,itisallowedto ru nwithinth eplace.h e a g e n texecuteswithinth eav aVirtualM achine ,n dqueriesth eplaceo rth erequested data.h eplacein turn verifiesth eauthority of th eagent basedo n th esecurityclassificationofth erequested data.f th eagent'sauthorityal low sit to retrieveth einformation requested,th eplacequeriesth edatabaseo nthatcomputer ,andprovides th ea g e n twithth erequested information .lternatively,placesnremoteerversknow a b o u ts imilarplacesn dan refera user's a g e n tto those placesif th eagent ' s t icketallows.

B. I N F O R M A T I O N F L O W T h i ssectionusesascenarioto examineth einformation flow b e t w e e n th estationary

an d mobilea g e n t m o d u l e sof th eproposedsystem.

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p r e p r o g r a m m e dn dt rained"oocatenformat ionro m particularlocations,asedn priorusean duserfeedback.o w e v e r ,h eplannercanmodifytheL R Eparametersby selectingth elink,if h eh asparticularinstructions for thismission.

T he J T F398strike planner k n o w s thatheneedsth eIntelligence L R E to pa yparticularattentiontoheuildingsorthofth eandingtrip.fterelectingth entelligenceR E button,a M A R K agentconfigurationscreen appears,ass h o w n in Figure7.

I & < - 3 j i j 3 : j : ; pmmKtkJteenwtEKpEsmNtetaeS8rtJ*few*&>te&s*s|

Intelligence Weather

IntelligencThis istheIntelligenceAgentInterfacefarstrikeoperations.Pleasesettheparametersbelowtoconfigureyouragents.

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appropriatedatato satisfyth erequest. Ifitisfound,th eL R E passesth edatab a c kto th e C C A to b ec o m b i n e d with the returneddata from other L R E san dformattedfo r th e user.

Ifth edatao nth ebuild ingsnor thof th elandingtripisno tavailablelocally,th eIntelligenceL R EcallsaH o m ePlacetop a w namobileagenttoearchremoteregions. T h eIntelligenceL R E passes thequeryan dconfigurat ion parametersb a s e donth eplanner'sinputfrom th econfigurat ionscreen.h eH o m ePlacecreatesamobilea g e n tandissuesaticket,th eagent'sauthority, the permit an d th esearchquery,as shown in Figure8.

/

\

TicketIP addressTranspor t n e t w o r kT i m elimitfo r tripA uthor i ty Electronicsignature/certif icate P ermitsA l l o w a n c e

T i m eto live M axsizein bytesProcessor timelimitsR i g h t to executecertain instructions Listofplacesit mayberedirected to A uthor i t iesit mayinteract with Electronicsignatures/certif icates ofall remote agentsit mayinteractwith Query

Figure 8.InformationPassed from H o m e Placeto Mobile AgentT h eH o m ePlaceestablishesaSecureSocketsL a y e rsessionwithth efirstserver

o n th eagent'sticket,in orderto allow th ea g e n tto t ravelsecurely in accordance withth e protocolspresentedin C h a p t e rIV .h enth eprotocolsh a v eb e e nsatisfied,th emobile a g e n tleavesth elocalregion an d travels to th efirstticketdestination.po n arrivalat th e serverplace,th emobileg e n tisxecutedwithintheJ a v avirtualm a c h i n e . T h ea g e n t

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Q u e r y Result

Back -EndA g e n tFormatsqueriesfo rlocaldata-source LocalData S ource

QueryR e s u l t Figure9.InformationTransferB e t w e e nM o b i l e AgentandRemoteDataSource

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Req ues tfo rstrikedata to CentralControlA g e n t( C C A )C C A doesanalysisof request

\C C A callsIntelligenceLocalR e sid e n tExpert(L R E )A g e n tIC C A passestasking toIntelligence L R E \IntelligenceL R Esearcheslocaldatabasefo rdata If findsdata,L R E passesdata to C C A YIf failsto find data,Intelligence L R Ecallsth eH o m ePlace tospawn a mobilea ge n tT h e H o m ePlacedirectsa mo b ilea ge n tto pre-identified databases,issuinga ticket,authorityan dapermit .assesth equeryto th emobileagentMobileA g e n tleaveslocalregionan d travelsto1s t ticketdestinationMobileA g e n t passesauthority to th eserver remoteplaceinsideJa vavirtualma c hin e

R e m o t e P lacepassesbacka permit withlocalrestrictions an ditsauthority IMobileA g e n tverifiesan dauthenticatesremote placeauthority YMobileA g e n tpassesqueryto R e m o t ePlace

R e m o t ePlace(front-end agent)passesqueryto R e m o t eLR E(back-end agent)fo rProcessing R e m o t e L R Eformatsquery fo rlocaldatasourcean dexecutesqueryR e m o t eL R E takesresponse toqueryan dha n d sit back toR e mo t e PlaceR e m o t e P lacedelivers to Mo b ileA g e n tMobileA g e n treturns to H o m ePlace

IMobileA g e n t passesits authority to th eH o m ePlaceH o m ePlaceverifiesMobile A g e n tauthorityan dpassesits ow nauthority YMobileA g e n t verifiesan dauthenticates H o m ePlaceauthorityYMobileA g e n tpassesqueryresultstoH o m ePlaceY H o m eP lacepassesdata to Intelligence L R E

IntelligenceL R Epassesoata to C C A YC C A combinesdata withother L R E reportsIC A formatsdatafo rlocalb ro w se rYC C A passesdata to localbrows er

Figure11. Scenario InformationFlow Diagram

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requested information .h a ta g e n t then sharesth eresultsofit ssearch w i t h th eotheragentswait ingatth eregionserver.h em o b i l eagentsthenreturnh o m e .rafficin th eupperregions il leimitednde t w o r kerformance il lm p r o v etllevels.h is a r r a n g e m e n t is illustratedgraphicallyin Figure12 .

2.trategiesto Limit BandwidthRequirementsT h e r ere n u m b e rofstrategiesthatcanemployedtoeduceth eb a n d w i d t hrequirements thatM A R K putso n th esystem asa w h o l e .a. Consolidation ofcommoncode T h e r eisadivis ionoflaborb e t w e e nth ecooperativeagentsin th eproposed

architecture.obilegentsrea skpecific,arryingpecificueriesndrocessing specifictypesofinformation .h e ym aycarrys o m elevelofd o m a i n k n o w l e d g ewiththem to enable r e m o t eprocessing ofdata,w h i c hallowsagentsto eliminateredundantdatain th e field,withoutcarrying it acrossth enetwork. emote placesar einformation specificagents ,specializednhandl ingth enformat ionvailabletthatnode .tisossibleourtherreduceth esizean db a n d w i d t hrequirementsofmobile ,taskspecificagentsbyeliminating c o m m o n m o d u l e sfrom th eprogranimingof th em o b i l ea g e n tan dstoring thosem o d u l e son r e m o t ea g e n tservers .W h e nana g e n tneedsto execute instructionswithin agivenm o d u l e ,itinvokesth em o d u l eb ym e a n sofaremoteprocedurecall.o rexample,communicat ions a m o n gm o b i l eagentsan db e t w e e nmobileagentsan dplacesisacapabilitysharedb yallagents .fth ep r o g r a m m i n g m o d u l e sthatcontrolc o m m u n i c a t i o n sarestoredateachplace,thentheresoeedo rmobilegentstoarryth eode.nly h i g hlevelontrolling m o d u l e sandm o d u l e sthatareuniqueto th etasksofaparticulara g e n tshouldb ecarried across th e network.

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b.ncapsulationversusagentconnect ionsA smobileagentsgatherdata,encodingan dencapsulating itfo rt ransport ,

th eiz eofth ea g e n tg r o w swithth equantityofdatacollected.h isanb eimi tedb y taking a d v a n t a g eofa g e n texpertisean d processing capabilities.sinformationis gathered itisprocessedo nr e m o t eservers.e w dataiscomparedwithdatath ea g e n th asobtained from othersources,an dredundantinformationiseliminated.h isreducesth equantityofdata th ea g e n t m u s tcarry,b utopens th esystem to th einformationsecuritythreatsdescr ibedin C h a p t e rIV .fit isn o tsafeto processinformationonr e m o t eservers,thenthereisno reason fo r th ea g e n t to encapsulateit an dcarryit acrossth enetwork.M A R K agentscanb e p r o g r a m m e dwith th ecapabilityto encryptreportsan dsendthem totheirH o m ePlaceviaconnectionservicesprovidedbyeach server .T hisreducesth e peakb a n d w i d t hrequirementsof th esystem byspreading the trafficov er t ime. istributingth e taskingoverseveraln o d e simprovesth esurvivabilitychancesofth edatagettingthroughb yreducingbot t lenecksin th en e t w o r k .h isstrategyalsoreducesth eloadonth eindividualserversb yspreadingit over multiple nodes .

c.roxyAgentServersIf th eagent'sH o m ePlaceisatth eend ofas mallpipe(e.g.,a wirelessL A N

atea),henitm ayb edvantageoustooom eprocessingofinformationo nasecure serverat tachedto th eh i g h e rbandwidth networksashore.h euseofaproxya g e n tservercanreduceth equantityof trafficonlo w b a n d w i d t hnetworksb yprocessingnformat ion remotely ,n dorwardingn lyh atnformationw h i c hsnique.igure3h o w sconceptualdiagram of a ProxyA g e n tServer .

In thisarrangement ,th eproxyagentserverreceivesqueriesfrom aclientat sea.h eproxya g e n tserverrunsaM A R KapplicationcompletewithaC C A ,L R E s ,an d mobile agentso n behalfof th eclient. ll processing isdoneremotely ,andonlyth eresults ar ereturnedtoth e C A runningonth eclientmachine.h isls oeducesconnect iv i tyrequirements ,sh elientoeso tneedoo m m u n i c a t ewithmobileg e n t shatre runningfrom the proxy.

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Figure 13.Proxy AgentServer

d. Persistence a nd cloning ofMobile AgentsSoftwaregentsaner o g r a m m e domonitornformationy s t e m so r

certain condit ions ,thenrespondw h e n thatcondition b e c o m e strue.o rexample,anL R E m ayb especificallytaskedto locateinformationonane n e m ytankbattalion.h em o b i l eagent tasked to find th edata m i g h t need to search communicat ions ,imagery ,a n d electronicsintelligencesources.h ea g e n tshouldmonitorthem all,b utdoingsorequirescont inuousm o v e m e n to v e r th enetwork,checkingeachsourceperiodicallyfo rchanges .nstead,whenth ea g e n tarriveso nth efirstserverandfindsth econdition to befalse,th ea g e n tcancloneitself,forwarding th ecloneto th enextserver,then"sleep"o nth eserverw h e r eitcurrently resides,waking periodically to se eif th econdition is true.ach n e w clonenowperiodically monitorsth econditionsateachserverw i t h o u tm o v i n gacrossth enetwork,n dw h e nth e

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tankbattalionm o v e sh egentsen deportsoheirH o m elace.h eeportsaneupdatedperiodicallyndw h e ntheresoo n g e ran e e dfo rth enformation,h eH o m ePlacesendsa m e s s a g eto th eagentsand theyd ie in place.

B. A G E N T S YS TEM TRAINING Intelligentoftwaregentsanearnnheirw nyollowingn depeating

operationsoneb yth euserandtailoring theirb e h a v i o rRef .8] .h eM A R Ksystem,beingc o m p o s e dof intelligentagents,willalsolearnasitisus ed.nitially,h o w e v e r ,th esystem n e e d sto betrained.ach m o d u l en e e d sto b eru n throughscenarios targeted atthatmodule'smission .

D uring th einitiala g e n ttraining,th eh u m a n usercanusehistoricaldata to verify th e accuracyof th ereturnedreport.h eu s e rshouldconcentrateo nth erecalla n d precisionofth eM A R Koutput.ecallmeasures h o w w e l lth ea g e n t slocatean d returnallavailabledata regardingth equery.yusinglocaldatabasesnacontrolledenvironment ,th eu s e rcan isolatedatalocatedbyth eL R Ean dth emobileagentsandprovidefeedbackaccordingly .Precisionmeasureshoww ellth egentsl iminatedi r relevantdatabeforeorwarding th e returnedreportRef.9] .gain ,sing controlledn v i r o n m e n to rth enitialtraining allowsh eu m a nusertosolateh o s epartsofth eystem thatn e e directe e d b a c kraddit ional t ra in ing/programm ing.

O nceM A R Kisdeployed,training isconducted each t imeth esystem is used.h em o r eftenitssed,h em o r eth egentsw i l learn.h eh u m a nusersh o u l dprovidefeedbackneveryepor teneratedb yh eys tem.A R Kh ash eapabilityofbeing tailoredto eachspecificuser,basedo nth eprofiledevelopedeveryt imeth eu s e rlogson.T h egentswillearnh a terson'sreferencesn desiresofw h e r en do watasretrieved.heres d a n g e rin this,o w e v e r ,thatth eystem willls oearnthatuser'sprejudicesan dbiases,therebyn o trealizingth efullpotentialofM A R K .o rexample,h u m a n userm ayh a v ew o r k e datth eNationalSecurity A g e n c y( N S A )an dbefamiliarwith th ematerialthey produce.h eh u m a nuserm a yt rainth ea g e n tto a l w a y sacceptmaterial

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providedb yNSAo v e rmaterialprovided byanothersource(i.e.,th eD efenseIntelligence A g e n c y ) ,even though N S A m ayn o tbeth ebestsourceofinformationfo rs o m equeries.n otherw o r d s ,th eh u m a ntrainsth esystemtoevelopb adhabits .m p r o p e rtrainingm ay cause M A R K to l imit it sscopeof resourcesan d mis svitalpiecesof information .

Perhapsabetterm e t h o doftrainingM A R Kisto promoteglobaltrainingb a s e don th eentireuserbase,ratherthanonpersonalizedtraining.eedbackfrom eachuserisn o tnecessari lytied to th euser profile,b utusedto trainth eentires ys tem.M A R K learnsfrom eachser'seedback,utppliesh a trainingcrossllsers,atherthann lyoh atspecificuser.h is al low sM A R K toincreaseth ek n o w l e d g ebaseofth eentiresystem,and perhapscompensatefo rindividualbiases.heglobaltrainingapproachprovidesM A R K th eopportunity to usecollaborativefilteringto predictw h a ti temsan e w userm i g h tlikebasednth ereferencesofsimilarusers.Ref.8]s n e w u s e revelops profile, M A R K comparesth ei tems thatth euser requests,o renters,to thoseofother users.M A R K thentriesto predictotheri temsth en ew userm i g h tw a n tto see.W h e nusingcollaborat ivefiltering,M A R K learnsbased on th ec o m m u n i t yof users.

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Figure14.U.S.IntelligenceCycleFrom R e f .[21]Processingofdatatakesplaceatalllevels.gentsconductpreliminaryprocessing

ofth edatato determineif itsatisfiestheirgoal. T h eL R E sprocessdataasitisreturned fromar iousgents ,ookingo redundanciesrnnecessarynformation. T h eC A receivesan dprocesses reportsfrom al lL R E s .h eC C A isthenresponsiblefo rintegrating al lth edataintoa finished product.heC C A disseminates th efinalreport to th euser an d to adatabasethattrackseachqueryandstoresinformation onth ereturneda n s w e rfo rfuture use. Ah u m a nuser,beforedisseminationorafter,ca nevaluateth einformationto ensurethatth ep r o d u c tofth eintelligencecycleis meeting th eneedsofth edecision-maker. T h e evaluatedfeedbackisincorporated intoth eintelligencecycle,trainingth eC C A an dL R E sandimproving th efinished product.

T h einishedroductm u s teailoredoh eser'seeds ,w h i c hh e C Aaslearned t h r o u g h multiple previous tasks.h eintelligencereport provided by th eC C A m u s t

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beobject iveandunbiased,n o t influenced by preconceivedideas,ascan happen withh u m a n analysts.h ereport,o w e v e r ,anbew a y e dorslantedin aparticulardirectionf th e user'srequestisn o tcarefullystated.h o r o u g han dfrequenttraining ofbothth eu s e ran d th eagentc an m i n i m i z e this problem.

T h eagentsdon o thaveto m o v esequentially through th efivesteps.W h e nth eL R E receivestasking from theC C A ,it does n o talw ayshav eto initiatecollection t h r o u g h m o b i l eagents .fth einformation is already held in localdatabases,th eagentsin th em o d e lskipth ecollectionstepan d proceeddirectly to processingor production.h is s av esprocessingt imean d decreases thedecis ioncycle t ime of th e user.

B. S U P P O R T F O R G E N E R A L M ILITARY I N T E L L I G E N C E A N D E S S E N T I A L E L E M E N T S O F I N F O R M A T I O N T w otypesofintelligenceessentialto preparingth ebattlespacefo rdecision-makers

includegeneralmilitaryintelligenceandessentialelementsofinformation(EEI).eneralmilitaryntelligencencludesnformationh atanesedorovidea c k g r o u n dinformationn ountry,retailednformationbout pecificrea.E Isrovidecriticalinformation about th eopponent or th eenvironmentthata decis ion-maker requiresto c o m b i n ewithotherinformationw h e n planning a particularoperation.M A R Kc a nb eused to m a n a g eboth typesof intelligence requirements.

1 .eneralMilitary IntelligenceUpdatingeneral ilitaryntelligenceaneeryi m e - c o n s u m i n g ,equiring s o m e o n etoesearchth ere aofinterestndupdateu m e r o u satabases.orxample,M A R K ,runningatatheaterlevel,canmaintaindatabaseswithupdatedinformat iono nacontinuousreriodicasis,ependingnse requirements.nformationoncerning s o m ereasfth ew o r l dm aynlyequirepdatingveryw e e k ,whilethersequire informationto becollected everyhour .W h e nusersat th etactical,operationalo rstrategic levelneedth einformation,agentscanbesenttoretrieveth egeneralmilitaryinformat ion from th e theater leveldatabases.

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Operationalplansm ayh a v estandingEEIs ,addressingintelligencenecessarypriorto th einitiationofoperations.M A R Kcanbetailoredto th eintelligencestaff,to beusedto provideupdatedinformation to plannersl m o s timmediately .tth etheatero rstrategic level,eachO P L A N canbeassignedaC C A thatisstoredin th eC A R .heC C A canbep r o g r a m m e d to update th eO P L A N EEIsona periodicbasis,determinedb yth ethreatlevelin th earea.achL R EcanbeassignedaparticularE E Ito m a n a g e ,usinglocaldatabasesandagentssdescribedinC h a p t e rVtoocateth erequiredintelligence.tm ayn o tb e w o r t h w h i l ein termsofprocessort imeands toragespaceto h a v eallO P L A N E E I supdatedallth et ime.tm ay beverybeneficial,h o w e v e r ,to h a v eth eO P L A N C C A availablein th e C A Rndou ncenariosoraintccasionally.h e C An dR E searnh r o u g h repetition andfeedback.

A sh eE Isrepdated,h eC Aano r m a th enformationoompleteh e IntelligenceEstimatefo r th eintelligencestaff(seeA p p e n d i xA ).h eIntelligenceEstimate templateanetorednh erow s er ,l lowingh eC Aoresenth eequested information in aformatusefulto th euser.h eIntelligence Estimateis continually revisedan dupdated asth esituationchanges .M A R K can providecontinuousinformationflow and updatesoh entelligencestimate.h isl lowsh entelligencetaffandh eorcec o m m a n d e rto h a v ea t imely ,up-to-datereportat their fingertips toaid in decis ion m aking.

C. D A T A F US ION A N D I N T E G R A T I O N Fusionis definedin Webster ' s Dictionary as th em e r g i n gofdifferentelementsinto

a union.ata fusion attemptsto takeinformationfrom multiplesourcesan du seit to m a k einferencesabout theenvironmentexternalto th esensors,creating asinglepicture.u m a n sdohighlycomplexdatafusionallth et ime,assimilating inputfrom th esensesto createan understandingofeventshappeningaround them.u m a n sare alsoableto takeinformation theyh a v eccumulated ,omparettoh eurrentituation,ndm a k ennferencerprediction of futureevents .

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Data FusionEnvironmentCol l e c ti onAssets (Agents)

1L Pre l i mi na ryFiltering- t ime - location- type-signature

LevelOn eP r ocess ingObjectRefinementD a t a a l i g n m e n t-spatialalignment - tempor alreference -unitsofmeasur e D a t a association - associatinginfor mation with th etaskP osit ionalfusion- combining infofrom var ioussour cesto developa t imelineor posit ion

LevelTw oP r ocess ingSituationRefinement Identify an d

developpatterns in th edata

LevelT hr eeProcessing ThreatRefinementDataValidity E valuationofindications an d war nings HE

LevelFour Processing Process RefinementR e q u e s t fur thercollectionFocusth eeffortsofLevels OTIPthroughThr e e :IECollectionM a n a g e m e n t-agent availability -tasking prioritization

Output toNextLevel-Human or computer

Figure16.AgentSupportedData F usion M o de lT h edatafusione n v i r o n m e n tcanoccuratth eL R Elevel,asitreceivesit s

tasking from th eC C A andanalyzes data from th eagents,or at th eC C A level,asit receives it s tasking from th eh u m a n andanalyzesdata from th eL R E s .or purposesofthisexample, a s s u m ethatth eL R E is receiving taskingfrom th eC C A and that th eagentsh a v eth eabilityto d os o m e preliminary filtering.

b . Preliminary FilteringT h epreliminaryfilteringincludesmakingureth enformationfitswithin

th et imeandlocation identified in th equery.h eagentensuresthatth eeventdescribedin th equery responseanswersth equestionasked(i.e.,shipsar e m o v i n gnorth,n o tsouth).h e a g e n tls onsuresh a theres ignaturenh eata,dentifyingheourcefth einformat ion an d th elocation from w h i c h it w as retrieved.or th eagent to doth efiltering,it m u s tb ebletoufferdataandh a v eccessoh eerver 'somputingp o w e rtooh e comparisons .h is concernsth eissuesofa g e n tlearningandintelligence.o rth ea g e n tto d oacomparison b e t w e e ndata,itm u s th a v eth eability to determinew h i c hinformationis m o r erelevant to it smission .

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D a t aassociationrefersto combining allof th einformationthatbelongsto aparticular problem or query.h eL R E performsdata associationb y taking th edata returned from multiplemobileagentsan dformingitintointelligencethatcanb eassociatedwitha particularobjecto r target.h eL R E alsodoess o m epositionalfusion,c o m b i n i n gdatafrom variousourcesoevelop imelinefeventsrocationofanbject.e d u n d a n tinformat ion is identified and discarded.

d . LevelTwo -Situation RefinementInLev elw o,h eR Evaluatesh eatathath aseeneturnedb yh e

agentsandfrom th elocaldatabases.ttriesto putit intoanorder(b yt imeorbysubject)thatisapplicabletoth esituation.h eL R Ealsolooksfo rpatternsin th eatathatm a y identifyelationshipsetweenntities.sh eR Eo m b i n e sh eataro marioussources ,tsoingsituationenerali2ation,"w h i c hA n t o n yefinessaottompabstractionofinformationfo rth epurposeofsituationwarenesswithrespecttoi g h e rlevel-of-abstraction entities"[Ref.22] .o rexample,a n a g e n treturnsi m a g e r yofan L C A C heading a w a yfrom th ebeach.heL R Ek n o w sthatL C A C sare typicallyassociatedwith ships. iven thefact that th eL C A C w asseenon th ewater ,th ei m a g e r yindirectlyprovidesevidenceof a h i g h e r level-of-abstractionentity (a ship).

T h eR Esls ooingsituationpecialization,"hichso po w n reasoningorth epurposeofdeducingrinferringubordinatelementsorentities.o rexample,th ea g e n treturnsinformationaboutth elocationofanaircraftcarrier.h eL R E k n o w sh a tircraftarriersarryirplanes.l thoughh eR Eo eso tav epecific confirmation ,tannferh existencefo w e revel-of-abstractionlementsth eairplanes).h eirplanesm ayenobservedrnobservableecauseh eyrenh e h a n g e r bay.

D ependingnth eophisticationofth eoding,h eL R Eanalsot temptsituationabstraction,w h e r eitattemptstofillin missinginformat ionb a s e donreasoning.T h eo m b i n e dutputfituationeneralizationn dituationpecializationm a yo tprovideacomplete picture,as theyusereasoning b a s e do ndirectlyobservedobjectsRef .

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22] . Situationabstractionusesm o r eo m p l e xnferencendreasoninglgori thmsnn at tempt to " m i m i cth ereasoning performedby h u m a n experts."Ref .11 ]

e.evelThree-Threat RefinementT h eL R Ec o n d u c t sthreatr e f i n e m e n tfo rit sareaofexpertise.tlooksat

historicalxpectat ions,bject ives,ntentsn dapabilities.hreatref inementprovidesinformat iono n possiblee n e m yintentan d friendly forcevulnerabili t ies.o rexample,th ew e a t h e rL R E receivesinformationa b o u tmultiple b a dw e a t h e rareasin th eS o u t hPacific.Itaneviewhistoricalr e n d sndth eharacterist icsofth ew e a t h e rnh a tre ao rparticulart imeofyearan dprojectth el ikelihood thatatyphoonwillform.tcanalso providerojectednformationb o u tpathsthatth ey p h o o nmaytake,ncludingw h i c h areasmayexpectth emostd a m a g e .

T h eC C A does threat refinem ent bycombining al lof th edatafrom theL R E s intoo nepicture.

/evelFour-Process RefinementT h eL R E doesprocessrefinementb yredirecting agentsto gatherm o r edata

if itn e e d sfurthercollectionfo ranalysis .fth eL R Ek n o w sthatitwillneedaparticularpieceofinformat ion to a n s w e rit stasking,it cancontrolth en u m b e rofagentsspawned an d directtheiractionsb a s e do nag l o b a lcollectionstrategy.h ishelpspreventth ew a s t eofresourcesnunnecessarya tacollectioni.e.,hotgunninggentsoew sgenciesh a tconcentrateon th eM i d d l eE a s t w h e n th eL R E n e e d sinformationa b o u t G reenland) .

Collect ionm a n a g e m e n tresponsibilitiesresidetachlevelofth em o d e l .T h eL R E prioritizesit stasksb a s e d oninputfrom th euserandth eC C A .h eL R E passesth eprioritization to th eH o m ePlacet h r o u g hth et imeto liveallowancein th epermitfo rm o b i l egents .h eC Am a n a g e saskingrioritizationorh eR E sasednh epreprogrammed m o d u l e s ,directuserinputanda g e n tavailability.fth euserentersanad h ocquerythatd o e sn o tdirectlytranslatetoaspecificL R E ,th eC C A willassigntasking

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S u m m a r yO f Time-Sensitive PlanningPhasesPhase I PhaseII PhaseIII PhaseIV PhaseV PhaseVISituation Crisis Co urseof Co urseof Execut ion Execut ion D e v e l o p m e n t A s s e s s m e n t ActionDevelop ment ActionSelection P lanning E v e n t E v e n t C I N C ' S C J C S C J C S C I N C N C A occurswith R E P O R T / sends presents refined Receives decides to possible national A S S E S S M E W A R N I N G an d prioritized A L E R T execute O P O R D security NT resolved O R D E R COAstoNCA O R D E R orimplications P L A N N I N G O R D E R Action M o n i t o r Increase Develop C J C S C I N C C J C Ss endsw o r l dsituation awarenes s C O A s advice to N C A develops E X E C U T E R e c o g n i z e Increase C I N C C J C Sm ay O P O R D O R D E R b y problem reporting assigns tasks to send P L A N I N G Refine authority of S u b m i t J Sassesses subordinates O R D E R to T P F D D S E C D E F CINC's situation b y evaluation beginexecution Force C I N C A S S E S S M E N T T S advice*;on request planning before preparation exercises J kJ C 4 ^ 1 V UvJ v/J-1 mes s age fo rma lselection O P O R D possible military

action Cre a t e /modify TP FDD ofC OA b y N C A J O P E S database N C A - C J C S

evaluat ion S T R A N S C O M preparesdep loymentestimatesvaluate C O A s

maintained P E C reports execution statusegin r e d e p l o y m e n tp lanning

O u t c o m e As s es s that N C A / C J C S C I N C N C A select C I N C s ends Crisis e v e n tm a y h a v e decide to develop sends C OA O P O R D resolvednat ional military C O A s C o m m a n d e r ' s C J C S R e d e p l o y m implications Estimate with releases C OA en t offerees R e p o r t th e r e c o m m e n d e d selection b y e v e n tto C OA N C A in A L E R T N C A / C J C S O R D E R

Figure17 .Summary OfTime-Sensi t ive PlanningPhasesFrom [23]

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Scenario:. .Upon receipt of th e report from th efield,th e jointstafflaunches theirers ionofM A R Kossistnh entelligencendeconnaissance gathering process.heyalsobrief th eC J C Sandth eN C A o nth esituation.W i t ho thh eointtaffndh eI N C ' staffunningA R K s imultaneously ,th eintelligencereturnedverifiesPacifica'sintentions.W i t h USforcesb e c o m i n gincreasinglyvulnerablein th eregion,th eNCAdecidestopursuemilitaryoptionstoonductnoncombatantevacuationoperations ( N E O )oeth epproximately50 0A m e r i c a n su tofPacificaeforefurthermilitaryaction can be taken. T h eC I N C continuesto m o n i t o r th esituation t h r o u g hM A R K ,looking atall availablesourcesof intelligence,weather,new s ,etc... 3.ourseofAction DevelopmentT h ereporting C I N C isresponsible for developing an d submit t ingC O A sto th e N C A

as militaryoptionsto counterth esituation.uringthisphaseC O N P L A N san dO P L A N s developedspartof th eeliberateplanningprocessan dtoredin th eointD e p l o y m e n t SystemJ D S )rexaminedoetermineftheyanesedossistnh eO A development .argemountsofinformationreassede t w e e nh elayersn v o l v e dduring theC O A development .im eiscriticalduring thisphase.

T h i sphasesompletednceth e IN C preparesndubmitsisCommander'sEstimatealong with h is r e c o m m e n d e d C OA to th eC J C S .

Scenario:..TheC I N C ' sstaff beginsto developC O A sfo r th e N E O .n th ed e v e l o p m e n tofth eO A s ,h etaffplannerspullh eN E Om o d u l ero m M A R K ' S A R an dloadtintoth eystem.h eplannersn p u tdatainto M A R K ,uchasth elocationandapproximatet imeofexecution.A R K automaticallyaunchesh eppropriategentsoin dnformat ionconcerningspectsofthisarticularN E O .O Lm a r tecidesoen d M A R K to lookatPacifica'snewspapersource,Libertad,to seeif any recentn e w sevents point to thisswitch in loyalties.M A R K returnsa listofarticlesthats h o w th eleaderofPacificarecentlyh adas o m e w h a tdiscretevisitwith th eleaderofIslandia.M A R K alsolooksatC O N P L A N sandO P L A N sin th eJ D Sdatabaseto se eifan yofthemloselyresembleh epcomingperationasedpo nh einputfrom th eplanners.sM A R Kreturnsth erequiredinformat ionto th e planners,it feedsthatinformation intoadatabasethatisused to assistin th e

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Scenario:..At845Z,h eN C Aivesh ereeni g h toxecuteh eoperat ion at1100Z.h e plannersand M A R K havecontinued to m o n i t o r th e situationn dh a v eoticedthatth eliteoastalefensepecialperation forceaseenulledoffth ehip.h eyretagingutside oronao c o u n t e rh em o b satheringroundh eapitalnppositionoacificasiding withIslandiainsteadofth eU.S.M A R Kfeedsinformationintoth eO P O R D processto account for thischangeofthreat .h estrikewillnowb e tailoredacknto upportingoleo rh eN E O .h eperationeginspreciselyat100Z,withM A R Kin fulloperationto m o n i t o ran ypossible c h a n g e sand to feed th einformationto th er ightelement.h ew e a t h e rdataprovided b yM A R K confirmsan upcoming storm in thearea,bu tshould n o taffecth eperation.A R Kisplays igitalictureakenyn u n m a n n e derialehicleiv e inutesfterh eperationo m m e n c e ss h o w i n g th ed e m i s eof th e1 0 0 0 -man eliteforceand th esuccessfulentry intoth ecountrybyU .S.forces.M A R K continues to provideinformationas th eoperat ionunfolds...

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a.eneral Magic'sOdysseyG eneralMagic'sevelopmentfmobileg e n te c h n o l o g ym o s tlosely

m a p soh eequirementso rM A R Ksiscussednh ishesis.o rheirnitialimplementat ionofagenttechnologyGeneralM a g i ccreatedanewp r o g r a m m i n gl a n g u a g ecalledTelescript[Ref.4] .t w asbuiltfrom th eg r o u n du pto suppor tth ecreation ofm o b i l esoftwareagentsand enabled th emobileagent concepts presentedin thisthesis.elescriptis n o tascriptinglanguage .atheritisa completeobjectoriented p r o g r a m m i n g language thatallowsdevelopers to i m p l e m e n tth e m a j o r c o m p o n e n t sofmobileagents .

T h eTelescriptengineisasoftwareprogram similarin c o n c e p tto th eJ a v a vir tualmachine.trovides protectedre aorgentsoun .ik eh eav air tualm a c h i n e ,tsnbstractionayerthatnterfaceswithh eperatingystemofth eostcomputer .tdoesno tallow directaccessto th ehardware,peripheralsan ds torageof th e h o s t m a c h i n e .

G eneralM a g i casnableoustainupporto relescript,n dts d e v e l o p m e n tnvironment ,abriz g e n t W a r e ,r imari lyueo ackfevelopersinterested in building applications in anothern e w p r o g r a m m i n gl a n g u a g e .G e n e r a lM a g i crecognizedthatwidespreadadoptionofJ a v apreventedeneralcceptanceofTelescript.Tabriz w asa product basedonTelescriptanddesigned to supplement w e bservers.eneralM a g i ch asw i t h d r a w nbothproductsfrom th emarket ."Ref .2]eneralM a g i cistaking a d v a n t a g eof th elargeinstalleddeveloperbaseofJ a v aandh asm p l e m e n t e dtheira g e n ttechnology in " 1 0 0 % PureJ a v a "using J a v a classes.a v a providesmostof th efunctionalityof th eTelescriptlanguage,b uts o m ecapabilitiesare n o tsupported in version1. 1of th eJ a v a virtualmachine.pecifically,J a v adoesn o tcurrently provideaw a yto captureth estateofanexecutingprogram.dysseygentsm u s trestartateachdestination,rexecuten ly specifiedm e t h o d sateachdestination.nanat tempttoo v e r c o m ethisw e a k n e s s ,G e n e r a lM a g i cevelopedth eideaofanO dysseyw ork er .h ew o r k e rclasss subclassofth e a g e n tlassh atu nsneaskperestination.Aw o r k e rs etoftasksn d etofdestinations.teachdestination th eworkerexecutesto complet ionallofth etasksonit s

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listfo rthatestination. Thesetasksnddestinationsan bemodifiedsnformat ionis gatheredduring theagent's travels .[Ref.12 ]

b . IBMAgletsIB M h asdoneasignificanta m o u n tofagentresearch andd e v e l o p m e n tan d

offersan u m b e rofdemonstrat ionsthrough th eInternetandin applicationssuchasL otus Notes .o rh em o s tart,BM'sgentesearchasocusedntationarygentsh a toperateo n eitherth ecliento rth eserver.neareaofresearchwith directimplicat ionsfo rM A R Ko w e v e r ,she gletsWork bench,nd glets uildingnvironment .ik e G e n e r a lMagic'sO dyssey,heseoolsrese dbyeveloperstoreatemobileoftwareagents.

T h eA g l e t sW o r k b e n c hs isualnvironmento rreatinggent-based applications.t consistsof th efollowingcomponents :[Ref.13 ]glets,J a v a classlibrariesand tools to enable objects to m o v eodax,a high levelJ a v a library to I B M ' sD B 2 database D B C ,an dO D B C - s t y l elibrary to R D B sazza ,a visualG UIbuilder for J a v aT h e s eoolsrovidesefulevelopmentnvironmentorreatingg e n t

applications.ik eth eG eneralM a g i cimplementa t ion,IBM'sA g l e t sare J a v athreadsthatar eapablefunningnn ya v anabledrow s er .ls oik eeneralMagic'simplementat ion ,IBM'sJ a v a A g l e tsare n otcapableofmaintainingstatew h e ntraveling.w o r karoundlikeG e n e r a lMagic'sw o r k e rclassm aybeusefulin partiallyovercoming this weakness .

2.ommercially Available Plug-ins A teasttw oroductsnth emarketnowappeartoav eignificantpotentialo

extend th efunctionalityofM A R K b yproviding aplugandplayinterfaceb e t w e e nm o b i l esoftwaregentsrlaces,ndhirdartyearchtilities. Verity'search'97sn

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drawn from acatalog of 28 ,61 3images .h eapplication then returnsotheri m a g e sfrom th ecatalogwith s imilarshape,color,an d texture.

Suchanapplicationcouldbeveryeffectivein retrievingatellitem a g e r yfrom adatabase. isualR e t r i e v a l W a r eapplicationsc anb ecreatedasplug-ins to aM A R K system thatinterface with a g e n tplaceswhere thea g e n t requestsi m a g e r ys imilarto asampleeither carried b y th eagent,o rstored locally in anindexofsamples.

B. R E L A T E D G O V E R N M E N T P R O J E C T S 1. Intelligent Decision Aids(IDA)T heIntelligentD ecisionA i d sproject[Ref.2 5]sa jointeffortb e t w e e nth eA r m y

Research L aboratoryandG T E Laboratories.inceth eArmy'sc o m m u n i c a t i o narchitecture ischangingfrom primarilyvoicedominatedtom o r edata/ informationdominated , n e e d w asecogniz edorovidenutomatedecisionupporterviceoetterssistc o m m a n d e r s .

Theirrchitecturalonceptsoeparateerviceontrolunctionsi.e.,ecision supportpplications)ro mh a tfresourcesrovidedyh existingo m m u n i c a t i o n snetworks,mult imedia serversan dinformationservers .hesedecision supportapplications willbesplitb e t w e e nServiceControlN o d e sS C N )irectlyattachedtoth enetworkan d ServiceClientsresidingin th eclientterminalsatth ec o m m a n d e r ' slocation.T h ekeypointof their architectureisth en e w conceptofanS C N .h eS C N isa network-based intelligentagentocatedetween,ndavingccesso,xistingnformationerversn detwork resources.tactsasag a t e w a yo rfilterb e t w e e nclientsandth einformationtheyseekto eliminateredundancyan dfusedatafrom variousinformationservers .h isnewapproachattemptsto reducebandwidth requirementsb yconsolidat ingandfusingdatafrom multipleinformation serversand providingthatdata to clients.

T h eSC N monitorsth en e t w o r kstatus(bandwidthus age ,throughput ,etc.)andcan reallocateb a n d w i d t htoclientssinceth en e t w o r kprotocolusedb e t w e e nth eS C Nandth eclientisasynchronoustransferm o d e( A T M ) .h isisparticularlyimportanttodaybecause

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th em o u n tfata,ideo,udio,tc.,oingcrossetworksontinuesoncrease drastically while b a n d w i d t h limitations remain asa m a j o r constraint.

2.ir eEngagementAnalysis Too l( FE A T 4)F E A T 4s rototype,ollaborativelanningystemevelopedyh eMarine

C orps ,aspartofth eS eaD r a g o nP r o g r a m ,withassistancefrom th eC A D R e s e a r c hC e n t e rofCalifornia PolytechnicStateUniversity.h egoalofthissystem istoprovidefo rreal-t imeecisionupportnth ebattlefield.naddit iontoecisionsupport,th er a m e w o r k provideso rituationalwareness , issionnalysis,ntelligencereparationfh ebattlefield,an dcooperat iveplanningacrossfunctionalareasthroughth euseofintelligentsoftwareagents.h eg e n t sesidingnh eE A T 4workstat ionsnh experimentalC o m b a tOperationsC e n t e r( E C O C )ofth eM a r i n eCorpsC o m m a n d a n t ' sWarfightingL ab ( C W L )cont inuously m o n i t o r factorsrelated to th eplanning andexecutione n v i r o n m e n tan d providethat informat ionto th euser.

F E A T 4h aserviceagents ,m e n t o ragentsandh u m a nagents .h eystem usestotalofs e v e nserviceagents :n g a g e m e n t ,mission,weather ,terrain,m o v e m e n t ,ogisticsandnetwork.g e n tinteractions are initiated an dcoordinatedt h r o u g hana g e n tkernelthatallowsh egentsoeceiven do stnformationnw h a tsescribeds emanticnetwork.Asemanticn e t w o r kin thiscontextcontainsth ecurrentstateinformationofth eagentin objectform.h ea g e n t kernelalsocoordinates th ec o m m u n i c a t i o nb e t w e e nagents .M e n t o rg e n t sepresentoldiers,eaponystems,anks,tc .hesegentsrovideinformation b a c k to th eF E A T 4workstat ionsasto theirstatus.h eh u m a na g e n tinterfaces with th esystem t h r o u g h th e workstations.Ref .2 6 ]

3.ntel l igentInformation DisseminationServer (IIDS)T h entell igentnformat ionDisseminat ionerverIIDS)roject,ponsoredy

D A R P A ,isane n h a n c e m e n tofanearlierprojectk n o w nasth eBattlefieldA w a r e n e s sand D ataDisseminat ionB A D D )nformationDisseminat ionerverIDS).h eriginalD S g