DTIC ADA576883: Acquiring User Models to Test Automated Assistants

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Acquiring User Models to Test Automated Assistants
MarcPickett1 and DavidW.Aha2 and J.GregoryTrafton2
1NRC/NRLPostdoctoralFellow;Washington,DC20375
2NavyCenterforAppliedResearchinArtificialIntelligence;
NavalResearchLaboratory(Code5510);Washington,DC20375
(marc.pickett.ctr|david.aha|greg.trafton)@nrl.navy.mil
Abstract
Table1:MethodforAcquiringandUsingUserModels
Acentralproblemindecisionsupporttasksisoperatorover- 1.Gathertracesfromhumanoperatorsinaninitialsubjectstudy.
load, in which a human operator’s performance suffers be- 2.Acquireusermodelsfromtraces.
causeheorsheisoverwhelmedbythecognitiverequirements • Extractfeaturevectorsfromtraces.
of a task. To alleviate this problem, it would be useful to • Constructanexpertoperator.
provide the human operator with an automated assistant to • Hobbletheexpertoperatortomatchuserfeaturevectors.
sharesomeofthetask’scognitiveload.However,thedevel- 3.Evaluatetheusermodels.
opmentcycleforbuildinganautomatedassistantishampered • Learnauserclassifier.
bythetestingphasebecausethisinvolveshumanuserstud- • Extracttracesfromtheusermodels.
ies,whicharecostlyandtime-consumingtoconduct.Asan • Attempttofooltheclassifierwiththemodels’traces.
alternativetouserstudies,weproposeacquiringusermodels, • Ifthemodelfailstofooltheclassifier,gotoStep2.
whichcanbeusedasaproxyforhumanusersduringmiddle 4.Iterateassistantdesignusingtheusermodels:
iterations, thereby significantly shortening the development • Build/modifyanautomatedassistant.
cycleforrapiddevelopment.Theprimarycontributionofthis • Usethemodelstotesttheassistant.
paper is a method for coarsely testing automated assistants 5.Testtheassistantwithhumanoperators.
byusingusermodelsacquiredfromtracesgatheredfromvar-
iousindividualhumanoperators.Weapplythismethodina
case study in which we evaluate an automated assistant for
We present an alternative method for this task: Derive a
usersoperatinginasimulationofmultipleunmannedaerial
vehicles. set of user behavior models (from data collected from an
initialsubjectstudy),usethemassurrogatestotestdifferent
designsfortheassistant,andfocussubsequentsubjectstud-
1 Introduction iesontheonesthatperformedwell.Thepotentialbenefitis
alargesavingsintimeandcost.
Operatorsofunmannedvehiclescansufferfrominformation
Inthispaper,wedescribethismethodforacquiringuser
overload,whichmayleadtolossofsituationawarenessand
models in Section 2 and its initial application in Section 3,
sometimes fatal consequences (Shanker and Richtel 2011).
where the goal is to assist users with controlling a set of
To address this, researchers have proposed using decision
(simulated)unmannedairvehicles(UAVs).Ourapplication
aids with two primary components: a cognitive model that
involves RESCHU (Boussemart and Cummings 2008) sce-
assessesanoperator’sstateinthecontextofthesystemthey
nariosthataredesignedtoelicitinformationoverloadcondi-
areusingtomonitorthesevehiclesandanautomatedassis-
tions.WedescriberelatedworkinSection5,thelimitations
tantthataidstheoperatorasneeded(Ratwani,McCurry,and
ofourmethodandfordrawinglessonsfromthissinglecase
Trafton2010).Forexample,whenthecognitivemodelpre-
study in Section 4, and conclude in Section 6 with sugges-
dicts that the user is distracted the assistant could draw the
tionsforfutureresearch.
user’sattentionandmakerecommendations,orevenexecute
recommendedactionsunderlimitedconditions.
2 Method
Typically, testing these decision aids involves assessing
their ability to assist operators in subject studies. However, An overview of the method for acquiring user models and
thesestudiesrequiresubstantialtimeandefforttodesignand usingthesefordevelopingautomatedassistantsisshownin
conduct,andasinglestudyislimitedtotestingatmostonly Table1.Inatraditionaldevelopmentcycle,developersbuild
afewdecisionaidvariants.Thus,ifmanydesignoptionsof anautomatedassistant,thentesttheperformanceoftheas-
the automated assistant need to be considered, testing each sistant by doing human user studies. Because user studies
allofthemcanbeprohibitivelyexpensive. are costly and time-consuming, this can lead to slow itera-
tions. In our approach, we use automated user models in-
Copyright(cid:13)c 2012,AssociationfortheAdvancementofArtificial stead of actual people for some of these iterations. User
Intelligence(www.aaai.org).Allrightsreserved. models are fully generative in that they are given the same
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Acquiring User Models to Test Automated Assistants
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14. ABSTRACT
A central problem in decision support tasks is operator overload in which a human operator?s
performance suffers because he or she is overwhelmed by the cognitive requirements of a task. To alleviate
this problem, it would be useful to provide the human operator with an automated assistant to share some
of the task?s cognitive load. However, the development cycle for building an automated assistant is
hampered by the testing phase because this involves human user studies which are costly and
time-consuming to conduct. As an alternative to user studies, we propose acquiring user models which can
be used as a proxy for human users during middle iterations, thereby significantly shortening the
development cycle for rapid development. The primary contribution of this paper is a method for coarsely
testing automated assistants by using user models acquired from traces gathered from various individual
human operators. We apply this method in a case study in which we evaluate an automated assistant for
users operating in a simulation of multiple unmanned aerial vehicles.
15. SUBJECT TERMS
16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF 18. NUMBER 19a. NAME OF
ABSTRACT OF PAGES RESPONSIBLE PERSON
a. REPORT b. ABSTRACT c. THIS PAGE Same as 7
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Prescribed by ANSI Std Z39-18
observations that a human user would be given, and must wesearchforfeaturesthathavehighvariancebetweenusers,
producespecificactionslikeahumanuserwould.Userstud- butlowvarianceforvectorsgeneratedbyasingleuser.
iesusingthesemodelsarefast,essentiallyfree,andrequire Givenasetofweightssignifyingtherelativeimportance
nohumansubjectsapproval(IRB).Thus,theyprovideafast ofindividualfeatures,webuildamodelbybuildingan“ex-
approximation to humans to quickly ferret out significant pert” operator, then hobbling it so that feature vectors ex-
problems with the automated assistant. The questions arise tracted from its traces match those generated by a partic-
ofhowthesemodelsareacquired(Steps1and2),howmuch ular human operator. The assumption here is that individ-
thesemodelsoperatelikehumanusers(Step3),andhowthe ual humans would perform perfectly except for their con-
modelsmaybeusedinlieuofhumans(Step4).Finally,the straints,andtheseconstraintsaccountformuchofthevari-
goaloftheusermodelisnottoeliminatehumanuserstud- ation among individuals. For example, RELIEF might tell
iescompletely,buttoreducerelianceonthem.Becausethe us that “reaction time” is an important feature to emulate.
usermodelsareonlyapproximationstohumanusers,anyfi- If our expert operator’s reaction time is negligible, and we
nalassistantshouldbetestedusingauserstudywithactual read a set of traces for user u whose average reaction time
humans(Step5). is 310ms (with a standard deviation of 20ms), we restrict
ourexpertoperatortorespondtoanevents95%ofthetime
Step1.GatheringTracesfromHumanOperators.
between270and350ms(i.e.,whatwouldbestatisticallyex-
Step 1 is to gather traces from human operators. A trace is pectedgivenaGaussianwithµ=310ms,σ =20ms).Note
a recording of a user’s actions and the observations of the thattheexpertoperatorneednotperformoptimally.
environmentgiventohimorheroverthecourseofasingle
trial in the decision support task. We collect n trials (typi- Step3.EvaluatingUserModels
callyabout10)fromeachofmusers(8or9,butmorewould
Step 3 is evaluating the user models produced by Step 2.
bepreferable).Weassumethatthesetrialsareindependent.
We would like some assurance that our user models actu-
Thatis,weassumethatthereisnosignificantlearningorfa-
ally operate in the same style as the specific human users
tigueausergeneratesduringhisorherntrials.Inpractice,
theyaremeanttoemulate.Wetestwhethertheusermodels
we reduce the amount of in-trial learning by allowing the
operate like humans by using a sort of automated “Turing
userampletimetofamiliarizehimorherselfwiththetask
Test”. That is, we build a classifier that recognizes individ-
beforerecordingtheirtraces.Asidefromauniquerandomly
ual human users by their traces, then we see whether this
assigned ID number, we record no identifying information
recognizerclassifiesthemodelforuseruasu.Iftheclassi-
abouteachuser(e.g.,genderorage).
fierthinksthatthemodelforureallyisu,thenthemodelis
Step2.AcquiringUserModelsfromTraces saidtopasstheautomatedTuringTest1.
Given n feature vectors for each of m users, building a
Step2isacquiringmodelsfromthetracescollectedinStep
classifierisastraightforwardmulticlasssupervisedlearning
1.Givenasetofm·ntraces(ntracesforeachofmusers),
problem.Wedoleave-one-outcross-validationonasuiteof
weacquireamodelforeachuserbyextractingfeaturevec-
classifierswithdifferentparameters,choosingtheclassifier
tors from his or her traces, then building the model so that
andparametersthathasthebestaverageperformanceonthe
thefeaturevectorsextractedfromthemodel’stracesaresim-
testset.
ilartothoseextractedfromtheuser’sactualtraces.Thus,we
Given a classifier and a set of feature vectors for user u,
canviewoursetofusermodelsasvariationsofasinglepa-
we build a model for u as described in Step 2. With this
rameterizedmodelthattakesasinputasetoffeaturevectors,
model,wegeneratettracesandextractfeaturevectorsfrom
and interacts with the environment to generate similar fea-
thesetraces.Wethensendthesefeaturevectorstotheclassi-
turevectors.Thebenefitofthisisthatavarietyofusermod-
fierandcountthenumberoffeaturevectorsthattheclassifier
elsallowsustotesthowanautomatedassistantwillperform
classifiedasbeinggeneratedbyuseru.
fordifferenttypesofusers.
The feature vectors we extract should be broad enough If our classifier has low accuracy for classifying human
to encompass the user’s interaction style. For example, if users,weshouldextractmorefeaturesfromtheusers’traces
we simple extract the trace’s performance score (but no (resultinginlongerfeaturevectors).Iftheclassifierhashigh
other metric), it might be possible to achieve this score accuracy,butthetracesgeneratedbytheusermodelareeas-
withwidelydifferentinteractionstyles(especiallyforlower ilydistinguishedfromtheactualhumantraces,thentheuser
scores).Withmanyinterdependentfeatures,itbecomesless model is said to fail the automated “Turing Test”, and the
likelythatwecangenerateamodelforauserthathasthese model should be modified accordingly to better mimic the
feature vectors, but operates in a markedly different style user.
fromtheuser.
1Weusetheterm“TuringTest”inquotestodistinguishitfrom
Once we have extracted our traces and feature vectors
its more common usage involving human-level AI. Some impor-
fromthem,wewillwanttoknowwhichofthesefeaturesare
tantdifferencesbetweenour“TuringTest”andthemorecommon
most useful for characterizing our users and distinguishing
variantarethatours(1)usesacomputer(notahuman)asjudgeof
themfromeachother.Weuseamulticlassfeatureweighting
similarity,(2)comparesusermodelstootherusermodels(notac-
algorithmtohelpusidentifywhichfeaturesarebestfordis- tualusers),and,ofcourse,that(3)theobservationsinvolvestyles
tinguishingindividualusers(inthecasestudybelow,weuse ofoperation,notachatterminalwherethejudgeisallowedtoin-
avariantofRELIEF(KiraandRendell1992)).Essentially, teractwiththeusermodel.
Step4.DesigningAnAssistantUsingUserModels
Step 4 is using the user models (acquired by Step 2 and
proven acceptable by Step 3) to iteratively design an au-
tomated assistant. We assume that we have built an initial
automated assistant, and we would like to test it. Because
it has the same inputs and outputs as a human user, a user
model’sinterfacewiththedecisionsupporttaskisthesame
asthatforahuman.Therefore,totestausermodelweonly
need to define its interaction with the automated assistant,
then run the assistant on various users with varying user-
model/assistantinteractions.
Since our initial human user traces were from a system
thathadnoautomatedassistant,wemustguesshowthehu-
mansmightinteractwiththeassistant.Wecanparameterize
this.Forexample,wemightexpectthatahumanusermight
taketheassistant’ssuggestionsppercentofthetime,where
p is an adjustable parameter. The results of running a vari-
ety of users and parameters on an automated assistant can
then be analyzed to inform the next iteration of automated
assistant.
Figure 1: Screenshot of the supervisory control simula-
Step5.TestingtheAssistantwithHumanSubjects tion.ShownareUAVs(largebluecircles),targets(greencir-
cles),hazards(yellowconcentriccircles),waypoints(small
Step 5 is testing the final automated assistant with real hu-
bluecircles),andprojectedflightpaths(blacklines).When
man operators. This step is the same as in a traditional de-
aUAVarrivesatatarget(asthetwoUAVshavenearthetop
velopmentcycle.
ofthescreen,eachshownasabluecircleinsideagreencir-
cle),itremainsmotionlessuntiltheuserinstructstheUAVto
3 CaseStudy:AMultipleUAVControlTask
engagethetarget,atwhichpointthetargetisacquired(and
Toillustrateourmethod,weacquiredusermodelsforacon- anewtargetappears),andtheUAVisrandomlyassignedto
trol task involving multiple UAVs, then used them to test anunassignedtarget.(Seetextfordetails.)
an automated assistant for the same environment. In this
case study, we investigate whether our method results in a
plausibleinteractionbetweenanautomatedassistantandour (NOP). If a UAV passes through a hazard area, it accumu-
usermodels.Inparticular,wehypothesizethat(1)increased
lates damage. After a certain amount of accumulated dam-
use of the automated assistant improves the user models’
age,aUAVisincapacitated.Eachnon-incapacitatedUAVis
performance, and (2) the increased performance is more
assignedtoatarget,andmovesalongitsflightpath(asdeter-
pronouncedformodelsoflower-performingusers.Further-
minedbythewaypointstheoperatorhasinserted)atafixed
more,wehypothesizethat(3)ouruserclassifierwilloutper-
rateuntilitarrivesatatarget,whereitwaitsfortheuserto
formarandombaseline,and(4)ourusermodelwillbeable
engage the target. When a target is engaged, a new target
to“passtheautomatedTuringTest”(fooltheuserclassifier)
appearsatarandomunoccupiedlocation,andtheengaging
moreoftenthanarandomusermodel.
UAVisrandomlyassignedtoanunassignedtarget.
TheRESCHUSimulator
CaseStudy,Step1.GatheringTraces
For our case study, we use a variant of a simulator pro-
We collected 10 traces from each of 8 human users. Each
vided by Cummings et al. called Research Environment
tracerecordedtheuser’sactionsandtheobservationsavail-
for Supervisory Control of Heterogeneous Unmanned Ve-
able to him or her while operating RESCHU for a 5-
hicles (RESCHU) (Nehme 2009), (Boussemart and Cum-
minuteperiod.Thoughweretainednoidentifyinginforma-
mings 2008), which simulates a task controlling multiple
tionabouttheusers,eachuserisreferredtobyanindexfrom
unmannedaerialvehicles(UAVs).
1to8.
The operator’s goal in this simulation is to engage as
many targets as possible during a 5-minute trial. A screen-
CaseStudy,Step2.AcquiringUserModels
shot of the simulation is shown in Figure 1. The simula-
tor includes 5 UAVs, multiple targets (of which exactly 7 Because the number of possible observations a user
arealwaysavailable),andhazardareas(ofwhich12areal- may receive in RESCHU is large (∼10124), building an
waysactive).Auser’savailableactionsaretochangeUAV observation-actionmodel,suchasthatusedby(Sammutet
target assignments (change target), insert waypoints al.1992)and(Sˇuc,Bratko,andSammut2004),wasinfeasi-
(insert WP), move waypoints (move WP), delete way- blewithoutasignificantamountofstateabstraction.Instead,
points(delete WP), instruct a UAVto engagea targetat we followed the method described in Step 2 by extracting
which it has arrived (engage target), and do nothing feature vectors from the 80 traces, and using RELIEF to
thetargetsgreedily(usingchange goals).
Table2:FeaturesandTheirRelevance
3. else if a UAV’s flight path crosses a hazard area, reroute
Feature Weight
averagedistancetohazardbeforeaction .71 theUAVaroundthehazard(usingadd WP).
tally:change goal .49
This simple operator performs well, averaging a score of
bigramtally:engage target→change goal .29
27.3targetsacquiredper5-minutesession,comparedtoan
bigramtally:change goal→engage target .23
average of 21.7 targets acquired per session for the human
tally:engage target .18
users.Thehighestscoringhumanuseraveraged24.5targets
bigramtally:change goal→add WP .13
bigramtally:change goal→change goal .12 persession.
tally:delete WP .12
Hobbling the Expert Operator This expert operator
averagedistancebetweenUAVs .12
served as a basis for constructing individual user models.
tally:add WP .09
tally:change WP .07 Based on Table 2, we constrained our expert operator to
bigramtally:add WP→change goal .06 mimic the user’s average distance to hazard before an eva-
bigramtally:add WP→add WP .05 sive action, the user’s average total number of change
bigramtally:change goal→delete WP .05 goals, and the user’s average UAV idle time. For exam-
averageUAVidletime .04 ple,theexpertoperatormightdelayengagingatargettobet-
bigramtally:change WP→engage target .04 termatchtheuser’saverageUAVidletime.Wechosethese
bigramtally:engage target→add WP .02
featuresbasedontheirRELIEFweightingsandtheireaseof
bigramtally:add WP→change WP .01
implementation.
bigramtally:add WP→delete WP .01
scoresperaction .00
CaseStudy,Step3.EvaluatingtheUserModel
tally:NOP .00
averagetimebetweenhazardcrossandactiontofix .00 Sincethereisagooddealofinterdependenceamongthefea-
(Allotherbigramtallies) .00 turesinauser’sfeaturevector,wehypothesizethatitwould
bedifficulttogenerateatracethatproducesasimilarfeature
vector,butthatoperateswithamarkedlydifferentstyle.We
help judge which features are most distinguishing for our implementthesubstepsofStep3andlearnaclassifierthat
8users(i.e.,wewantfeaturesthathavehighvariabilitybe- distinguishes human users by their feature vectors, extract
tweenusers,butlowvariabilitywithinvectorsgeneratedby tracesfromtheusermodels,thentestwhethertheclassifier
asingleuser).WechoseRELIEFoverotherfeatureweight- candistinguishhumanusersfromtheirmodels.
ing algorithms because of its popularity and ease of imple-
Learning a User Classifier For each of our 8 human
mentation.
users, we have 10 vectors of 36 real-valued features. Thus,
Feature Vector Extraction From each trace, we gener- learningaclassifierisastraightforwardapplicationofmul-
ated a set of 36 features. These include tallies over the 6 ticlasssupervisedlearning,withtheclassesbeingthe8indi-
primitiveactions,talliesoverthe25“bigrams”forthe5non- vidualusers.Wetrainedasuiteof35classifiersusingleave-
NOPactions(whereA→BisthenumberoftimesBwasthe one-out cross-validation. Included in this suite were vari-
firstnon-NOPactionafterA),theaverageproximitytoahaz- ous paramaterizations of Bayes Logistic Regression, Naive
ardaUAVreachedbeforeauserreroutedtheUAV,theaver- Bayes, Gaussian Process Classifier, k-Nearest Neighbors,
agedistancebetween(or“spread”)oftheUAVs,theaverage Least-Angle Regression, Sparse Multinomial Logistic Re-
timeaUAVwasidle(howlongbetweenwhentheUAVar- gression, Elastic Networks, and Support Vector Machines
rivedatitstargetandwhentheuserengagedtheUAV),the (SVMs) with various kernels. The best of these classifiers
averagenumberof(non-NOP)actionstheusertookdivided for our data was an SVM with a polynomial kernel (Poly-
bytheuser’sperformancescore(asameasureofactionef- SVM), which yields an average of 62.5% accuracy on the
ficiency),andtheaverageamountoftimeauserwaitedbe- test set (compared to 12.5% accuracy for a random classi-
tweenwhenaUAV’spathcrossedahazardareaandtaking fier).
actiontoreroutetheUAV.Notethatthenumberofengage
Fooling the Classifier Using the user model, we gener-
target actions is the same as the performance measure
ated 10 new traces for each of our 8 users with the aim
(the number of targets engaged). These features are shown
ofmimickingthatuser.Poly-SVM’sclassificationaccuracy
inTable2sortedbytheirRELIEFscore.
was27.5%onthesetraces.Thatis,givenatracegenerated
Construct an Expert Operator Since reaction time is a bythemodelforuseru,theclassifiercorrectlyidentifiedthe
limitingfactorinhumanuserperformanceforRESCHU,we trace as coming from user u 27.5% of the time. A random
wereabletobuildasimplerule-based“expert”operatorthat classifier gives us 12.5% accuracy. Given our classifier, we
outperformedourbesthumanoperator.Theexpertoperator considerapracticalupperlimittobe62.5%becausethisis
usesthefollowingrules: theaccuracyonactualhumantraces.
Given these numbers, the user models are said to have
1. ifthereisaUAVwaitingatatarget,engagethattarget.
donepoorlyontheautomated“TuringTest”.Inpractice,we
2. elseiftheUAVtargetassignmentsare“suboptimal”(us- would want both the classifier and the model’s accuracy to
ingagreedynearesttargettoUAVcalculation),reassign bemuchhigher,andwewoulditeratemoretimesonStep2
4 Discussion
All four of our hypotheses from the beginning of Section
3 were supported by our case study: (1) increased use of
the automated assistant resulted in increased performance
of the user models, (2) the performance increase was more
dramaticformodelsoflower-performingusers,(3)ouruser
classifier was correct an average of 62.5% on the test set,
better than 12.5% for a random classifier, and (4) our user
models passed the automated “Turing Test” by fooling the
classifier27.5%ofthetime,betterthan12.5%aswouldbe
expectedforarandomclassification.However,wehopethat
thisperformancecanbeimproveduponinfuturework.
Since the expert operator performs so well, we might be
tempted to simply let the expert operator take over (thus
completely automating the task), but doing so would break
therequirementthatthehumanoperatormustmaintainsitu-
ationawarenessofthetask.
Figure2:HowtheAssistantHelpedtheUserModels.In- 5 RelatedWork
dividualusermodels’performancesareshownaslightlines, This work differs from most work on Imitation Learning
withtheaverageperformanceshownasbold. and Cognitive Behavioral Modeling in that it attempts to
mimichigh-levelfeaturevectorsextractedfromusertraces,
ratherthantheprocessthatgeneratedthesetracesitself.This
toimprovethesefigures.Asthecurrentworkisademonstra- includes work using cognitive architectures, such as ACT-
tionofprinciple,wehopetoimprovethesefiguresinfuture R (Anderson 1993), to model user behavior such as driv-
work. ingautomobiles(Salvucci2006)orcredibilityjudgmentsin
micro-blogging (Liao, Pirolli, and Fu 2012). (For a survey
of work in Cognitive Behavioral Modeling, see (Zacharias,
CaseStudy,Step4.TestinganAutomatedAssistant MacMillan,andVanHemel2008).)
Previous work on behavioral cloning characterized the
In this step we describe how we built our automated assis- taskasastatemachine(e.g.,asaMarkovDecisionProcess),
tant,andhowweusedourusermodelstotestit. and mimicked the user’s policy (Isaac and Sammut 2003),
We implemented an automated assistant that constantly (Galata, Johnson, and Hogg 1999), (Floyd and Esfandiari
“suggested”anaction—theactiontheexpertoperatorwould 2010), (Argall et al. 2009). As in our case study, the num-
do— at every step of the session. The user (or the user berofpossibleobservationsinanenvironmentoftenmakes
model)thenhadthechoiceofexecutingaprimitiveaction, thisapproachimpracticalwithoutstateabstraction.Further-
or calling the assistant, which would then execute its sug- more, much of this prior work focuses on performing well
gestedaction. onaspecifictask,suchasflyingwell(Sˇuc,Bratko,andSam-
Wehadnomodelforhowuserswoulduseanassistant(as mut2004),(BainandSammut1995),insteadofattempting
therewerenoassistantsavailabletothemwhenwecollected toaccuratelymodelhumans.Thatis,thispriorworkfocuses
thetraces),soweparameterizedtheusermodels’interaction on maximizing task performance regardless of whether the
with the assistant. In particular, we introduced a parameter behaviorishuman-like.Toourknowledge,nopreviouswork
pthatdenotesthepercentprobabilitythatausermodelwill focusesonmodelingindividualusers,asourusermodelsdo.
calltheassistantatanyparticulartime. TheoriginaldevelopersofRESCHUhavelearnedauser
modelforRESCHU(BoussemartandCummings2008),but
Weranthe8usermodelsvaryingpfrom0to100%.Re-
thismodelisdescriptiveandcannotbeusedtogenerateac-
sultsareshowninFigure2.Individualusermodels’perfor-
tualoperationinRESCHU,unlikeourusermodels.Further-
mances are shown as light lines, with the average perfor-
more,thisworkdoesnotmodelindividualusers,butanag-
mance shown as bold. From this figure, we see that while
gregateofusers.
theincreaseduseoftheautomatedassistantimprovesperfor-
manceforallusermodels,theusermodelswiththelowest
6 Conclusion
non-assisted performance benefit the most from the model.
Whenp=0,eachmodelperformsasifthereisnoassistant. We have introduced a method for rapidly developing auto-
When p is set to 100%, the models all converge to operat- mated assistants by acquiring user models, and we demon-
ingidenticallytotheexpertoperator.Inpractice,p=100% stratedthemethodusingacasestudyofRESCHU.Though
wouldbefeasibleforourcasestudybecausethenumberof we’ve demonstrated the principle of the method, there are
non-NOPsuggestionsgivenbytheautomatedassistantaver- still many issues to be addressed. For example, we have
ages1every3.3seconds(andtheassistantrarelygivesmore not substantiated the hypothesis that a user model which
thanonenon-NOPsuggestionpersecond). matchesauser’sfeaturevectorsalsomatchestheuser’sstyle
of operation. One future correction for this is an analysis tation, MIT Dept. of Aeronautics and Astronautics, Cam-
oftheinterdependenceoftheuser’sfeatures,akintocross- bridge,MA.
validation.Forexample,wecanleaveoutthe“bigramtally: Ratwani, R.; McCurry, J.; and Trafton, J. 2010. Single
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