COMPUTATIONALLY ADJUSTABLE AUTONOMY
HENRY HEXMOOR
∗
AND BRIAN MCLAUGHLAN
∗
Abstrat. Reasoningabout autonomyisan integralomponentofollaborationamongomputationalunitsofdistributed
systems.Thispaperintroduesanagent-levelalgorithmthatallowsanagenttoontinuouslyupdateitsautonomywithrespetto
reurringasynhronousproblemswiththeaimofsystem-wideollaborationeieny. Thealgorithmisdemonstratedinarelevant
senarioinvolvingNASAspaestation-basedPersonalSatelliteAssistants,whihanhandledynamisituationmanagementthat
frustratesglobalollaborationprotools.
Keywords. Agents,Autonomy,portablesatelliteassistant.
1. Introdution. Computer-ontrolled systemsfeatureprominentlyin large-saleprojetsurrently
un-derdevelopmentby themilitary, ommerial, and sientiagenies. Examples ofthese projets inlude the
US military's Network-CentriWarfare dotrine, IBM'sAutonomi Computing initiative, and NASA's spae
stationprojet. Asthesesystemshaveinreasedinomplexity,self-governingomponentshaveometofeature
prominentlyintheirdesignandontrol. Thishangeinparadigmfromdirethumanontroltoindirethuman
oversighthasforeddesignerstoaddressissuesinvolvingtheautonomyofthesesub-systems.
Autonomyisdenedandusedinmulti-agentsystemresearh[6,7,11,12,13℄andotherdisiplinesinluding
soiology[10℄andphilosophy[14,15℄. It isimportantin multiagentinterationssineitrelatestheabilitiesof
anagenttoitsfreedomsandhoies. Theunderstandingandquantiationofanagent'sautonomyisrequired
foroherentinteragentinteration.
Theoneptof autonomyislosely relatedto theoneptsofpower,ontrol,anddependene [5,7℄. The
notionofautonomyhasbeenusedinavarietyofsensesandhasbeenstudiedindierentontexts. Itgenerally
presupposessomeindependeneor restriteddependene. However,itandesribemanydierentbut related
onepts. An agentanbeautonomouswith respet toanother agent ifitis beyond theinuenes ofontrol
andpowerofthatagent. Itanalsobeusedtodesribequalityofhoieandanevenenompassself-imposed
senseofduty onepts.
Whileautonomyanbeintuitivelyunderstood,itunfortunately isaomplextopiwhoseexatdenition
and implementation is rather elusive. However, by identifying types or sublasses of autonomy, spei
aspets of theonept anbe dened and quantied. The multiagent systemdesigner an then utilizethese
models to fous on the partiular attributes of autonomy that would be most beneial for the partiular
implementation.
Autonomyisdenedin[6℄astheagent'sdegreetowhihitsdeisionsdependonexternalsouresinluding
otheragents. ThisformofautonomyanbealledCognitiveAutonomy. Thisonepthasbeenexploredfurther
in[7℄. Thispaperutilizesthisdenitionofautonomyandpromotestherelativistiviewintroduedin [3,4℄.
Adjustableautonomyisarelatednotionthatapturestheideaofahumanoperatorinterveningandguiding
ationsofamahine[8℄. Anotherexampleoftheworkonadjustableautonomyis[1℄withquantitativemeasure
proposedin[2℄. Inthis,thedegreeofautonomyisdenedasanagent'srelativevotingweightindeision-making.
Thisapproahhasseveraladvantagesinludingtheallowaneforexpliitrepresentationandadjustmentofagent
autonomy.
The remainder of this paper presents our work regarding omputation and determination of adjustable
autonomylevelsfor ollaborative,problem-solvingagentsin amulti-agentsystem. Setion Twodesribesour
approah,inludingthegeneralizedalgorithm. SetionThree portraysanimplementationofthisalgorithmfor
NASA's PSA program. Experiments performed on this system are hroniled in Setion Four. Setion Five
presentstheonlusions drawnfromthiswork.
2. Approah. Thispaperaddressesadjustableautonomyin adistributed systemwhere agentsdisover,
announe,andompleteasynhronouslyourringtasks. Thetasksaregeneriandrequiremultiplepartiipant
ollaborationtosolve. Theollaborationproessisfailitated throughafour-stagebiddingproess:
1. Announement
2. Priority
∗
3. Permission
4. Aeptane
Inadditionto providing amehanismforollaborationontasks,thealgorithm mustbeabletosalewell
andhandle dynamiand omplexsituations. Thatis, itmust beableto handlemultiple,oniting tasks. It
mustbeabletohandlehangestotheproblemtopologysuhtheintrodutionorremovalofkeyagentsortasks.
Ideally,thealgorithmwillhandlevariationswithoutexessivesetbakin itsongoingomputations.
Announement
Upondisoveryofanewtask,thedisoveringagentknownhereastheoriginatingagentbroadaststhe
disoverytothe group. Eahagentmaintains alist of announedtasks. Thetask datastruture isshownin
Figure2.1.
An agentwill update the informationaboutatask asitreeivesrelevantinformation. Forsimpliation,
this paper assumes that all agents have some method of hearing announements and other bidding related
information,whether throughdiretor indiretmeans. Ifthissimpliation isnotthease,thealgorithm will
yieldasbestasolutionasispossiblewiththeinformationavailable.
TaskID
Loation
DisoveryTime
Originator
WorkerCountRequest
PriorityList
PermissionList
AeptaneList
Fig.2.1.TaskData
Priority
Uponreeiving andarhiving thetask announement, anagentwill reasonabout itsobjetivesuitability
toaddressthetask. Theagentmayinludeseveralattributes,e.g.,neessaryskills,energyusage,andthetime
that thetaskhas beenative. Itinorporates these fatorsin assigningsomemeaningfulprioritytothe task.
Itis importantto notethat,at thisstage,theagentwillnotaountforalternativetasks. Thatis, itwillnot
rankataskhigherorloweraordingtoitspersonalpreferenes. Reasoningalongsubjetiveonsiderationswill
ourlater. Upondetermining itspriorityforthetask, theagentwillannounethesoreto theother agents.
Inthe mostbasiversionof thissystem, onlytheoriginator needsto maintainallthe priorities. However, as
will be desribedlater, someenhanementsarepossiblein whih agentsanadjust theiraeptanebasedon
theprioritysoresmadebyotheragents.
Permission
Theoriginatingagentolletstheseprioritysoresandgeneratesapermissionlist. Initssimplestform,the
permissionlistisanorderedlistoftheprioritysores. However,thealgorithmutilizedbytheoriginatingagent
anbemuhmoreomplex,takingintoaountabstratoneptssuhastrustandanitytheoriginatingagent
hastowardspartiular agentsorevenknownsynergiesamong biddingagents. Ultimately,this permission list
ontainsthebiddingagentsintheorderofmosttoleastdesirableforjoiningthetask. Althoughtheoriginating
agentonly needsaspei numberof agentsto performthe task, it will reate anordered list ontainingall
bidding agentsin theevent that someofthemost desirableagentswill beunable orunwilling to partiipate.
Theoriginatingagentpublishesthislisttothegroup.
Aeptane
Unlike many ontemporary systemssuh as online autions, a bid doesnot onstitute aontratin this
system. Eah agentisallowedtotentativelyaeptorrejetthe permissiongrantedbytheoriginatingagent.
Additionally,atentativeaeptaneisnotenforeable. Ifanagentndsataskforwhihitismoresuitable,it
isfreetoabandonitsurrenttask. Aswillbeshownlater,itisassumedthattheagenthastakeninto aount
Thebiddingagentmakesheraeptanedeterminationbyaountingforseveralfatorsinludingitsdesire
or suitabilityfor this relativeto other tasks, thelevelof permission grantedby theoriginating agent forthis
andothertasks,andthepriorityofalternativeagentsshouldtheagentdelinetoperformthetask.
The bidding agent takes into aountompeting tasks at this stage rather than in the priority stage so
thatit anprovidebenevoleneforthesystem. Forexampleonsider anagentXthat hasplaedbidsontwo
tasks,Task1thathasbeenannounedbyagentAandTask2that hasbeenannounedbyagentB.AgentX
determinesitspriorityforTask1tobequitelow,butseesitspriorityforTask2tobehigh. BothagentsAand
BhavepublishedpermissionlistsinwhihagentXisamongthetophoies. IfagentXweretotakeagreedy
stane,itwouldaeptthetaskforwhihitgavethehighestpriority,inthisaseTask2. However,ifitfurther
inspetsthepermissionlists,it maydisoverthattheagentsthatwouldbeforedto performTask 1inagent
X's absene are not partiularly well-suited for the task and would struggle, while the alternativeagents for
Task 2areonlyslightlyless-suitable thanagentXand ouldstillperformadequately. Toprovide foroptimal
systemperformane,agentX ouldhoosetoaeptTask1eventhoughitwouldpersonallypreferTask2.
Therearethree aveatsto aeptingtasks. First,anagentmayonlygiveits aeptaneto onetask. If it
hasalreadyaeptedaprevioustask, itmustannouneitswithdrawalfromthat previoustask.
Seond,anagentannotaeptataskthathasbeenloked. Ataskislokedifnhigher-rankedagentshave
aeptedthetask,wheren istherequestednumberofagentsforthetask 1
.
Third, an agent annot aept a task where it is not ranked in the rst n non-rejeting agents in the
permission list where n is the number of agentsrequired to perform the task. That is, if a task needsthree
agents,andagentXisrankedfourth,itannotaeptthetaskunlessoneoftherstthreedelineit. Conversely,
any agent may deline a task regardless of its ranking in the permission list. These senarios are shown in
Figure2.2.
Task1:
#AgentsRequested: 3
Permission: {C,D, A,E,X,Y,Z}
Aeptane: {A,R,?,?,?,R,?}
Fig.2.2. AgentXannotaeptthetaskuntileitheragentAorErejetsit.
Algorithm
An algorithmhasbeendeveloped tofailitate thisbidding sheme. Thisalgorithm isimplementedat the
agentlevelandruns ontinuously. Thepseudoodeforthisalgorithmisshownin Algorithm1.
Some notes regardingthis algorithm. Inthe nal If statement, the agentdoes nothingif its hosen task
ould be lledby morequalied agents. This fores theagentto wait to see ifthe desired task will beome
available. As analternative,theagentouldhangethis toarejetion andrealulateaseond best hoie.
Then, if thedesired task beomes available due to top-ranked agentsrejetions, it anhange its aeptane
baktotheoriginaltask. Thisalternativekeepsallagentsbusy,butitmayauseadditionalstart-upostsfrom
hangingtasks
Itisthetaskoriginator'sresponsibilitytoensurethatthetaskdoesnotgetlostintheshue. Tothisend,
theoriginatingagentwill periodiallybroadasttheurrentstateofthetask.
Rather than rigidly dene the four phases of the bidding proess, the algorithm allows eah agent to
proeedindependently. Thispreludestheneedforoordinationofphasehangesthatmaybediultinsome
environments. However,thisouldausetheoriginatingagenttopublishapermissionlistbeforeallagentshave
giventheirprioritysores. Withthepubliationofthislist,theagentsarefreetobegintheaeptaneproess
before potentiallyidealagentsannounetheir priority. Topreventunneessaryshuingasnew agentsbump
outlessidealworkers,theagentsshouldtakepotentialshuingintoaountwhenbidding. Alternatively,ifthe
agents anommuniate with all other agentsin thesystem, then theoriginatingagentandelaypublishing
thepermissionlistuntilallagentshaveannounedtheirpriorities.
Toillustrate the algorithm, onsider thefollowingsenario. Tosimplify theillustration, thesenariowill
beshownfrom theperspetiveofthetasks.
Algorithm1BiddingShemePseudoode
while1do
Sensesurroundings
Task Listupdate
Appendnewdisoveredtasks
Appendnewheardtasks
Updateexistingtasks
forEahtasktinTaskListdo
Calulateandannounet
priority
if t
originator
==self thenCalulatet
permission
ListAnnounetaskt
endif
endfor
Calulatebestnon-lokedtask
forEahtasktinTaskListdo
if t
6
=
bestthenAnnounerejetion
else
if Selfrank
<
nth
non-rejetingthen
Announeaeptane
else
Donothing
endif
endif
endfor
endwhile
Agents Aand Bhavedisoveredand announedTasks1and 2, respetively. AgentsA, B, C,and D are
within respondingdistaneto thesetasks. Figure 2.3showsthestateofthetasksafter theagentshavebegun
to respond with their priority to the tasks and the originating agents have published permission lists. For
simpliity,permissionisgrantedbasedsolely onannounedpriority.
Task1 Task2
# AgentsNeeded: 2 # AgentsNeeded: 2
Priority: {A,7},{B,3},{C,8},{D,9} Priority: {A,1},{B,8},{C,2},{D,7}
Permission: D, C,A,B Permission: B,D, C,A
Aeptane: ?,?,?,? Aeptane: ?,?,?, ?
Fig.2.3.PermissionListPubliation
Withthepublishingofthepermissionlists,agentsarenowfreetobeginaeptingorrejetingthetasksas
shown in Figure 2.4. AgentsC andD arethe mostidealandidates forTask 1. C will aeptthis taskasB
aeptsTask2. Theywillquiklyrejetthealternatetasks.
However,Dhasbeenaeptedforbothtasks. Greedily,itouldaeptTask 1,but itsrejetionofTask2
wouldforeTask2tobeperformedbyA,averyunsuitableagent. Itmustdeideonaourseofationgreedy
orbenevolent.
Agent A annotannouneits aeptaneof Task 1 despiteits likely preferenetowardit. Rather, it will
wait toseewhatAgentDannounessothatitwillnothavetobeginitsineptperformaneofTask2andthen
possiblyswith mid-exeutionto Task1.
Next,onsider how thealgorithmwill reatto adynamisituation. Forthis weintrodueanotheragent,
Task 1 Task2
#AgentsNeeded: 2 # AgentsNeeded: 2
Priority: {A,7},{B,3},{C,8},{D,9} Priority: {A,1},{B,8},{C,2},{D,7}
Permission: D, C,A,B Permission: B,D, C,A
Aeptane: ?, A,?,R Aeptane: A,?,R,?
Fig.2.4.PartiallyAepted
Task1 Task2 Task3
# AgentsNeeded: 2 # AgentsNeeded: 2 # AgentsNeeded: 2
Priority: {A,7},{B,3}, Priority: {A,1},{B,8}, Priority: {A,7},{B,3},
{C,8},{D,9},{E,0} {C,2},{D,7},{E,5} {C,9},{D,8},{E,5}
Permission: D,C,A,B,E Permission: B,D, E,C,A Permission: C,D, A,E, B
Aeptane: ?,R,?,R, R Aeptane: A,?,?, R,? Aeptane: C,?,?,?,?
Fig.2.5. NewTaskandAgent
In this situation, C hooses its own task and rejets its previous aeptane of Task 1. Additionally, E
immediatelysends rejetion to Task 1due to itsabsolute inabilityto perform thetask asdemonstratedfrom
itspriorityannounementof0.
Thisleavesseveralissuestoberesolved. First,itallowsAtoaeptitsidealTask1asitisnowintherst
2non-rejetingagentsand doesnotneedtowait forD'srejetion.
Agent D is now desiredby all three tasks. It still has somedetermination to makebefore hoosing. For
instane,D'shoieoulddependuponwhetheragentAhoosesTask1orTask3. Italsodependsuponwhether
importanttaskswill beleftwithoutadequateworkers.
Theexatmethodutilizedfordeterminingitshoiedependsonhowmuhomplexitythesystemdesigner
imbuesin theagents'deision-makingproess. Idealeienyisadiultproblemthat ismostlikelybeyond
thepratialsopeofreal-worldagentsregardlessofthealgorithm.However,theagentouldplaytheprisoner's
dilemmagametoseond-guesswhatotheragentsmayhoose. Perhapsthesimplestandmostomputationally
eientmethodwhenfaedwithsuhinompleteinformationwouldbeforAgentD totakethegreedyhoie
andlettheotheragentsadjust tomaximizetheremainingsystemperformane.
Additionally, this example illustrates a problem with all task alloation algorithmsmaximizing utility
when notenoughworkersare present. If suh a senarioislikelyin thesystem, thedesigner ould inlude a
taskprioritythatwouldmodifytheagents'behaviorsuhthattheywouldbemorelikelytoaeptritialtasks
andleavelessvitaltasksunderstaed.
Despite the problems, this example demonstrates how the algorithm an adapt to hanges made
mid-alulation. Ratherthantossoutthebiddingproessandstartoverorexludenewagentsandtasksfromthe
proeedings,theagentsmakesomequikadjustmentsandontinue.
3. An appliation: The Personal Satellite Assistant (PSA). A PSAis a small (basketball-sized)
ying robot that is under development at NASA Ames (at the Moet eld AFB 2
) for deployment on the
internationalspaestation. Theserobotsare anoutgrowthof aneedto freeastronautsfrom routinetasks of
inventory ontrol,safety heks, and fault detetionand isolation. PSAs are loaded with avariety of sensors
inludingequipmentforgasandpressuresensing. Intheremainderofthissetionwedesribeanimplementation
ofouralgorithmthat allowsPSAs toperformseveralappropriatetaskssuhasreandgasleak(i.e.,on-and
o-gassing)detetionwhilereasoningabouttheirautonomyandlevelofollaboration.
Asperthealgorithm,thePSAthat detetstheproblemformulatesabroadastalert tosendto theother
PSAs. This is initiated when a PSA loatesan abnormality in its environment. The abnormality ould be
avariation in the ambient temperature or an atmospheri imbalane suh ashigh orlow pressure, orexess
oxygen,arbondioxide,ornitrogen. ThePSAsendsthealertontainingthetypeofproblemandtypeofroom
in whih theproblem is loated to persuade other agentsto help it pinpoint thesoure of theproblem more
aurately. Thisproessissimilar tothemethod usedinradio signaltriangulation.
Todetermineitssuitabilityforthis task,thePSAmustaountfor itsenergyresoures. Eah PSAhasa
limitedbatterypowerthatwillbeonsumedduringtransitaswellasduringthetaskexeution. Itisassumed
that thePSA hasameans ofevaluating itsresouresR, whih in this ase isits batteryharge. It will then
alulateitsostCtoperformthetask.
Cis initiallyomputed byalulatingthedistane to travelto thetask and thesubsequentdistane toa
powerrehargestation. ItdoesthesystemlittlegoodforaPSAtoassistinloatingaproblemonlytorunout
of energyand shut down. The totaldistane to bemoved is multipliedby theenergy onsumption rate. An
estimationoftheamountofenergyrequiredtoperformthetaskisaddedtogetthetotalostC.
C =(
Distane totarget+
Distanefrom targettoreharge)
×
EnergyConsumptionRate+
EnergyrequiredfortaskIf C
>
R then an unfavorable priority is return indiating unavailability. Otherwise, when C≤
R, thePSAansuessfullyhelp loatetheproblemand stillrehargeitself. Inthis ase,priorityP isalulatedby
rst onsidering what type of room in whih the problem is loated. This is done sine some loations are
inherentlymoreimportantthanothers. Forinstane,laboratoriesarerelativelylessimportantthantheontrol
enter. Additionally, the partiular anomaly deteted an inuene the priority for a partiular room. For
example,o-gassingofoxygeninaequipmentstoragemodulewould belessdisastrousthanthesameproblem
in ahabitation module. Conversely,high levelsof magnetiinterferenemaybedangerousfor theequipment
butouldbeoflittleonsequenetohumansinhabitingtheirquarters. Thedeterminedvalue,whihwedenote
asQ,isusedforalulatingthejob weightandisusedin thenal priorityalulationforP.
Q = ln(Time + RoomProblemFactor)
ThenaturallogisusedforthisequationbeauseitausesQtohangealongapreditableurveaseitherTime
orRoomProblemFatorinreases.
Pisomputedbyusingdistaneasasalarandomparingthenewjobweightto theoldjob weight.
P = Q
new
×
1
−
DistanetonewtargetMAXDISTANCE
−
Q
old
×
1
−
DistaneremainingtooldtargetMAXDISTANCE
MAXDISTANCEisthemaximumdistaneaPSAanmovethroughtheentirestation. Thedistaneplaysan
importantrolein thealulationofP.This isdue totheobservationthat thePSAwiththesmallestdistane
tomovewillbethemostlikelytoarrivequikest. Thus,thetimetoompletethetaskislowerwiththisPSA.
AsthePSAs proeedthroughthebidding proessprioritydelaration,permission,andaeptaneand
thehosenPSAs begintoarriveattheproblemloation,theywilltakeaprismonthefaeofthesearhspae
and begin sanning. This will allow PSAs that arrive quikerto beginthe searh proess, while PSAs that
arrivelateranhelprenetheresults. Thus,ameasureofompletionanbetakenatanypointintimeduring
thetriangulation.
4. Experiments. ExperimentswereperformedutilizingthePSAsenario. Theloationsofproblemsand
PSAswerearrangedsuhthat thesystemwasrelativelybalaned. TheQvalueofeahproblemwasrandomly
generated. ThenumberofPSAs inthesystemwassuientineahtesttomeet thedemands.
Theexatmethodofaeptanewas performedundertwostrategies. Instrategy1,agentshosetoaept
thetask inwhih theywerehighestrankedforpermission. Note that thisdoesnotneessarilymeanthat the
PSA greedily hooses thetask for whih it attributed the highestpriority. Rather, it will hoose the task of
theoriginatorthatmostvaluesthePSA'sassistane. Forinstane,ifaPSAislisted asrstinthepermission
list, it willaept that overatask where it islisted seond. Instrategy2, PSAs performasdesribed in the
algorithmtheyhoosetoaeptatasksuhthatthesumofallprioritieshosenismaximized. Thisstrategy
shouldspreadthequalityofhelparosstheproblems.
The results of the experiments are shown in Figure 4.1 and showsthat the two strategies produe very
similarresults. However,therststrategygivesslightlybetterperformaneinthispartiularsimulationandis
omputationallylessintensivein general.
The reasonfor this derease in performane lies in the nature of the deisionmaking in the systemasa
Fig.4.1.Qualityofhelpfortwostrategies
Fromtheperspetiveofautonomy,therststrategyrestritstheagentstoagreaterdegree. Theindividual
PSAshavelessfreedom inmobilityandhoieof tasks. Priorityonlyplaysarolein theveryrststage ofthe
proess. Afterthat,itisuptotheoriginatingPSA.TheseondmethodallowsthebiddingPSAs toundertake
whihevertaskisbothbestttingtothem andomplianttothegreaterneedsofthesystem.
5. Conlusion. As omputerontrolledsystemsinreasein omplexity,automatedollaborationof
sub-systemsbeomesmorerelevantandritialtosystemeieny. Utilizingtheoneptofadjustableautonomy
reasoningaboutommitments in partiularis a ritial omponent to solving this problem. This work has
shownhowreasoningaboutautonomyan formthebasisofmoment-to-momentommitmentmaking.
Wehave shown analgorithm that anbe utilized fordynami deision-makingthat is exible enoughto
handleagentsthat join orleavebefore tasksare ompleted,aswellasbeingabletohandle tasksthat appear
duringtheexeutionofothertasks.
Wehave shown how this algorithm an beimplemented in arelevant and urrentproblemthat of task
managementofNASA'sPersonalSatellite Assistantsonboard theinternationalspaestation. Thedomain of
PSAsisadynamienvironmentwheremultipleandpossiblyonurrentproblemsmaydevelop,andis anarea
thatwillbenetfromtheteamworkmadepossiblebythisalgorithm.
Future work in this areaantakemany diretions. Forinstane, we ould onsider subjetive attributes
suh asqualitiesofrelationshipsandsatisfationofagentswith thetaskassignmentproess. Additionally, we
anlook atthe appliation ofautonomydetermination in reasoningabout teams[3, 16℄and itseet onthis
algorithm.
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Editedby: MarinPaprzyki,NiranjanSuri
Reeived: Otober1,2006
