From: AAAI Technical Report SS-94-07. Compilation copyright © 1994, AAAI (www.aaai.org). All rights reserved.
Task EnvironmentCentered Design of Organizations *
Keith Decker and Victor Lesser
Department of Computer Science
University of Massachusetts
Amherst,
MA 01003
Email: DECKER@CS.UMASS.EDU
Abstract
The design of organizationsor other coordinationmechanismsfor groupsof computationalagents, either interacting with one another or with people, dependscrucially on the task environmentof whichthey are a part.
Suchdependenciesinclude the structure of the environment(the particular kinds and patterns of interrelationships that occur betweentasks) and the uncertainty in
the environment(both in the a priori structure of any
episode within an environmentand in the outcomesof
an agent’s actions). Designingorganizations also dependson properties of the agents themselves--butthis
has been studied morethoroughlyby other researchers.
Thecentral idea is that the designof coordinationmechanisms cannot rely on the principled construction of
agents alone, but must rely on the structure and other
characteristics of the task environment--forexample,
the presence of uncertainty and concomitanthigh variancein a structure. Furthermore,this structure can and
should be used as the central guide to the design of coordination mechanisms,and thus must be a part of any
comprehensivetheory of coordination.
This workingpaper will briefly describe our modeling framework,TASMS,
for representing abstract task
environments. Wewill also briefly describe a family of domain-independent,team-oriented coordination
algorithms called Generalizedpartial Global Planning
(GPGP).Havinga family of algorithms allows us
tailor the algorithmto the environment(or evena specific situation). Wewill give an exampleof an analysis
inspired by Burton and Obel’s workon organizational
structure and technologydecomposability.
Introduction
Wehave developed a task environment-oriented modeling
frameworkcalled T/EMS (Task Analysis, EnvironmentModeling, and Simulation)that features careful attention to the
quantitative computationalinterrelationships betweentasks,
to what information is available (and when)to update
*This workwas supported by DARPA
contract N00014-92-J1698, Office of NavalResearchcontract N00014-92-J-1450,
and
NSFcontract CDA
8922572.The content of the informationdoes
not necessarilyreflect the positionor the policyof the Government
andno official endorsement
shouldbe inferred.
32
agent’s mentalstate, and to the general structure of the task
environmentrather than single-instance examples[Decker
and Lesser, 1993d; Decker and Lesser, 1994b]. Our task
environmentmodelscan be used for both the analysis and
simulation of coordination algorithms, and also to design
organizational structures that are well-adaptedto particular
task environments.
The formof the TJEMSframework
is moredetailed in structure than manyorganizational-theoretic modelsof organizational environments,such as Thompson’s
notions of pooled,
sequential, and reciprocal processes [Thompson,
1967], Burton and Obel’s linear programs [Burton and Obel, 1984],
or Malone’s queueing models [Malone, 1987]. It is influenced by the importance of environmental uncertainty
and dependencythat appear in contingency-theoretic and
open systems views of organizations [Lawrenceand Lorsch,
1967; Galbraith, 1977; Scott, 1987; Stinchcombe,1990].
TASMSallows the quantitative operationalization of many
organizational-theoretic environmentalconcepts, especially
various dependenciesand uncertainties (see the decomposability example, from [Burton and Obel, 1984], later in
this paper) that are the basis of both contingency theoretic and transaction cost economic(i.e. [Williamson,1975;
Moe,1984]) viewsof organizations. Theobservation that no
single organization or coordination mechanism
is ’the best’
across environments,problem-solvinginstances, or even particular situations is also common
in the study of cooperative
distributed problemsolving [Fox, 1981; Durfeeet al., 1987;
Durfee and Montgomery,1991; Decker and Lesser, 1993b].
Keyfeatures of task environmentsdemonstratedin both these
threads of work that lead to different coordination mechanisms include those related to the structure of the environment(whatwe will call task interrelationships) and environmental uncertainty.
Uncertainty. Less uncertainty in the environmentmeans
less uncertainty in the existence and extent of the task interdependencies, and less uncertainty in local scheduling-therefore the agents need less complexcoordination behaviors (communication,
negotiation, partial plans, etc) For example, one can have cooperation without communication
[Geneserethetal., 1986]if certain facts are knownabout the
task structure by all agents. Sometimes
agents will not have
a priori informationabout the structure of an episode, but
they will be able to get information about a subset of the
structure after the start of problemsolving--nosingle agent
workingalone will be able to construct a view of the entire
problemfacing the group. Someof this uncertainty mayarise
fromdisparities in the objective (real) task structure and the
subjective(agent-believed)
task structure, either initially or
the situation evolvesovertime.
Taskinterrelationships. Taskinterrelationships include the
relationships of tasks to the performance
criteria by whichwe
will evaluatea system,to the control decisionstructuresof the
agents which makeup a system, and to the performanceof
other tasks. TheT2EMSobjective subtaskrelationship indicates the relationship of tasks to systemperformance
criteria;
the subjective mappingindicates the initial beliefs available
to an agent’s control decision structures; various non-local
effects such as enables and facilitates indicate howthe execution of one task affects the duration or quality of another
task. Eachrelationship is quantitatively defined. Whena
relationship extendsbetweenparts of a task structure that are
subjectivelybelievedby different agents,wecall it a coordination relationship. The basic idea behindGeneralizedPartial
Global Planning (GPGP)is to detect and respond appropriately to these quantitative coordinationrelationships.
The mainthrust of this workingpaper is to present TASMS
as a frameworkthat can be used for computationalorganization design; the remainderof this paper will give a brief
overviewof TA~MS,
a simple architecture for agents working
in a TEEMSenvironment,a brief overviewof the GPGP
family
of team-orientedcoordination algorithms, and an exampleof
a simple analysis. Finally wewill discuss howwe can extend
our analyses to more complex,hierarchical organizational
structures.
Short Overview of TASMS
Theprinciple purposeofa TASMS
modelis to analyze, explain,
or predict the performanceof a systemor somecomponent.
WhileT/EMSdoes not establish a particular performance
criteria, it focuseson providingtwokindsof multi-criteria performanceinformation:the temporalintervals of task executions,
and the quali~y of the executionor its result. Quali~yis an
intentionally vaguely-definedterm that mustbe instantiated
for a particular environmentand performancecriteria. Examplesof quality measures(whichcan be vectors) include the
precision, belief, or completeness
of a task result. T/EMSmodels describe howseveral quantities--the quality producedby
executing a task, the time taken to performthat task, the
time whena task can be started, its deadline, and whether
the task is necessaryat all--are affected by the executionof
other tasks.
A TA~MSmodelof environmentaland task characteristics
has three levels: objective, subjective, and generative. The
objectivelevel describesthe essential, ’real’ task structure of
a particular problem-solving
situation or instance over time.
Thesubjectivelevel describesthe agents’viewof the situation.
A subjective level modelis essential for evaluatingcoordination algorithms, becausewhile individual behaviorand system performancecan be measuredobjectively, agents must
makedecisions with only subjective informationJ Finally,
the generativelevel describesthe statistical characteristicsof
1 In organizational
theoreticterms,subjectiveperceptioncanbe
usedto predictagentactionsor ouqauts,whileunperceived,
objective environmental
characteristicsaffect performance
(or outcomes)
[Scott, 1987].
33
the objective and subjective situations in a domain.A generative level modelconsists of a description of the generative
processes or distributions from whichthe range of alternative probleminstances can be derived, and is used to study
performanceover a range of problemsin an environment.
A probleminstance (called an episode E) is defined as
a set of task groups, each with a deadline D(T), such
E = (TI,’T2, ¯ ¯ ¯, Tn). The task groups(or subtasks within
them)mayarrive at different times. Whiletask groupsare in2, the tasks within
dependentof one another computationally
a single task group are in general not independent.Figure 2
showstwo objective task groups, and Figure 3 showsagent
A’s initial subjective view of that task group. A task group
consists of a set of tasks related to oneanotherby a su btask
relationship that formsan acyclic graph (here, a tree). The
circles higher up in the tree represent various subtasks involvedin the task group, and indicate precisely howquality
will accrue dependingon what leaf tasks are executed and
when.Tasksat the leaves of the tree (withoutsubtasks) represent metho&,whichare the actual computationsor actions
the agent will execute(in the figure, these are shownas boxes).
Thearrows betweentasks and/or methodsindicate other task
interrelationships wherethe execution of somemethodwill
havea positive or negative effect on the quality or duration
of another method.The presenceof these interrelationships
makethis an NP-hardscheduling problem; further complicating factors for an agent’s local schedulerinclude the fact
that multiple agents are executingrelated methods,that some
methodsare redundant(executable at morethan one agent),
and that the subjective task structure maydiffer from the
real objective structure. This notation and associated semantics are formally defined in [Deckerand Lesser, 1993d;
Deckerand Lesser, 1994b].
Hospital Patient Scheduling Example
Let’s look at a brief exampleofa T/EMStask structure model
in termsof its ability to reasonaboutorganizationaldecision
making. The following description is from an actual case
study [OFetal., 1989]:
Patients in GeneralHospitalreside in units that are organizedby branchesof medicine, such as orthopedicsor
neurosurgeryEachday, physiciansrequest certain tests
and~ortherapyto be performedas a part of the diagnosis
andtreatmentof a patient. [... ] Tests are performedby
separate,independent,anddistally located ancillary departmentsin the hospital. Theradiologydepartment,for
example,providesX-rayservices andmayreceive requests
~oma numberof different units in the hospital.
Furthermore,each test mayinteract with other tests in
relationships such as enables, requires-delay (must be performedafter), and inhibits (test A’s performance
invalidates
test B’s result ifA is performedduringspecified time period
relative to B). Notethat the unit secretaries (as scheduling
agents) try to minimizethe patients’ stays in the hospital,
while the ancillary secretaries (as schedulingagents) try
maximizeequipment use (throughput) and minimize setup
times.
2 Exceptfor the useof the processor(s)
or otherphysicalresources
[DeckerandLesser, 1994b].
fNu rsing Unit 1
"~ fNursing Unit 1
method(executable task)
(~
task with quality
accrual function min
task already communicated
to ancillary
subtaskrelationship
....... ~. enablesrelationship
,---~
~-~-~
requires delay
inhibits
1
.
Figureh High-level,
subjectivetask structurefor a typicalhospitalpatientscheduling
episode.Thetoptask in eachancillaryis really the same
objectiveentityas the unit task it is linkedto in the diagram.
Figure 1 showsan subjective TAZMS
task structure corresponding to an episode in this domain,and the subjective
viewsof the unit and ancillary schedulingagents after four
tests have been ordered. Note that quite a bit of detail
can be captured in just the ’computational’aspects of the
environment--inthis case, the tasks use peoples’ time, not
a computer’s. However,TA~MS
can modelin more detail the
physicalresourcesand job shopcharacteristics of the ancillaries if necessary[DeckerandLesser, 1994b].Suchdetail is not
necessary for us to analyze the protocols developedby [Ow
et al., 1989], whoproposea primaryunit-ancillary protocol
and a secondaryancillary-ancillary protocol.
Weuse min (AND)to represent quality accrual because
general neither the nursing units nor ancillaries can change
the doctor’s orders--all tests mustbe doneas prescribed. We
have added two newnon-local effects: requires-delay and
inhibits. The first effect says that a certain amount8 of
time must pass after executingone methodbefore the second
is enabled. The second relationship, A inhibits B, means
that B will not produceany quality if executedin a certain
windowof time relative to the execution of A, and can be
defined in a similar manner.
Analysis in TASMS
The methodology we have been building uses the TA~MS
frameworkand other DAIformalismsto build and chain together statistical modelsof coordinationbehaviorthat focus
on the sources of uncertainty or variance in the environment
and agents, andtheir effect on the (potentially multi-criteria)
performanceof the agents. For example, we have used this
methodologyto develop expressions for the expected value
of, and confidenceintervals on, the time of terminationof a
set of agents in any arbitrary simple distributed sensor networkenvironmentthat has a static organizational structure
and coordination algorithm [Decker and Lesser, 1993b]. We
have also used this modelto analyze a dynamic,one-shot
34
reorganization algorithm (and have shownwhenthe extra
overheadis worthwhileversus the static algorithm) [Decker
and Lesser, 1993c]. In each case we can predict the effects
of adding moreagents, changingthe relative cost of communication and computation, and changing howthe agents are
organized (in this case, by changingthe range and overlap
of their capabilities). Theseresults wereachievedby direct
mathematicalanalysis of the modeland verified throughsimulation in T/EMS. Wewill omit the details here and refer the
reader to our papers.
An Agent Architecture
The T/EMSframeworkmakesvery few assumptions about the
architecture of agents--agentsare loci of belief and action.
Agents have somecontrol mechanism
that decides on actions
giventhe agent’scurrentbeliefs. Thereare three classes of actions: methodexecution, communication,and information
gathering. Methodexecution actions cause quality to accrue
in a task group (as indicated by the task structure). Communicationactions are used to send the results of method
executions(whichin turn maytrigger the effects of various
task interrelationships) or meta-level information.Information gatheringactions add newlyarriving task structures, or
newcommunications,
to an agent’s set of beliefs. Formally,
we write BtA(X)to meanagent A subjectively believes x at
time t [Shoham,1991]. Wewill shorten this to B(x) when
wedon’t have a particular agent or time in mind.
The GPGPfamily of coordination algorithms makes
stronger assumptionsthan TASMS
about the agent architecture. Mostimportantly, it assumesthe presence of a local
scheduling mechanismthat can decide what method execution actions should take place and when, which will be
described in the next section. It assumesthat agents do not
intentionally lie and that they believe whatthey are told.
The Local Scheduler
Each GPGPagent contains a local scheduler that takes
as input the current, subjectively believed task structure
and produces a schedule of what methods to execute and
when. It chooses these methods in an attempt to maximizea pre-definedutility measure;in this paper the utility
function is the sum of the task group qualities: U(E)
~7"~EQ(T, D(T)), where Q(T, t) denotes the quality of
3T at time t as defined in [Deckerand Lesser, 1993d].
Besidethe subjective task structure, the local scheduler
should accept a set of commitmentsC from the coordination
component. These commitmentsare extra constraints on
the schedules that are producedby the local scheduler. For
example, if method1 is executable by agent A and method
2 is executable by agent B, and the methodsare redundant,
then agent A’s coordination mechanismmaycommitagent A
to do method1 both locally and socially (commitmentsare
directed to particular agents in the sense of [Shoham,1991;
Castelftanchi, 1993]) by communicating
this commitment
to
B (so that agent B’s coordination mechanismrecords agent
A’s commitment,see the description of non-local commitments NLCnext).
The final piece of informationthat is used by the local
scheduler is the set of non-local commitments
madeby other
agents NLC. This information can be used by the local
scheduler to coordinate actions betweenagents. For example
the local scheduler should havethe property that, if method
Mxis executable by agent A and enables method M2at
agent B (and agent B knowsthis), then if agent A makes
commitmentto B to communicatethe results of M1by time
t, B will only scheduleM2after time t. 4 Thuswe maydefine
the local scheduler as a function LS(E, C, NLC)returning
a set of schedules S = {$1, $2,... , S,m,). Moredetailed
information about this kind of interface betweenthe local
scheduler and the coordination componentmaybe found in
[Garveyeta/., 1994].
and Lesser, 1992]. Module1 exchangesuseful private views
of task structures; Module2 communicatesresults; Module 3 handles redundant methods; Modules4 and 5 handle
hard and soft coordination relationships. Moremoduleshave
been designed, such as a load-balancing module. The modules are independentin the sense that they can be used in any
combination.
The GPGP Coordination
Modules
The GPGP
algorithmfamily specifies three basic areas of the
agent’s coordination behavior: howand whento communicate and construct non-local views of the current problem
solving situation; howand whento exchangethe partial results of problemsolving; howand whento makeand break
commitments
to other agents about whatresults will be available and when.5 The coordination mechanismsupplies nonlocal viewsof problemsolvingto the local scheduler,including non-local commitments
about what non-local results will
be available locally, andwhenthey will be available.
The five modulesdescribed in [Deckerand Lesser, 1994a]
forma basic set that providessimilar functionalityto the original partial global planningalgorithmas explainedin [Decker
3Notethat qualitydoesnot accrueafter a task group’sdeadline.
4Ofcoursein generala local schedulercannotbe optimal,and
this holdsfor human
agents as well. Wecan exploreother models
of local scheduling,
prioritization,attention,style, etc. [March
and
Simon,1958].Someworkis alreadybeingdonein this area [Lin
and Carley,1993;J.C. Kunz,1993].
5Theuse of commitments
in the GPGP
familyof algorithmsis
basedonthe ideas of many
researchers[CohenandLevesque,1990;
Shoham,
1991;Castelfranchi,1993;Jennings,1993].
35
Simulation
Example
Simulationis a useful tool for learning parametersto control algorithms, for quickly exploring the behavior space of
a newcontrol algorithm, and for conductingcontrolled, repeatable experimentswhendirect mathematicalanalysis is
unwarranted or too complex. The simulation system we
have built for the direct execution of modelsin the TtEMS
frameworksupports, for example,the collection of paired responsedata, wheredifferent or ablated coordinationor local
schedulingalgorithmscan be comparedon identical instances
of a widevariety of situations (generatedusing the generative
level of the model). Wehave used simulation to explore the
effect of exploiting the presenceof facilitation betweentasks
in a multi-agent real-time environmentwhereno quality is
accrued after a task’s deadline [Deckerand Lesser, 1993a].
The environmentalgenerative characteristics here included
the meaninterarrival time for tasks, the likelihood of one
task facilitating another, and the strength of the facilitation
(Od)"
In contrast to the previousanalytical work,in this section
wewill considerthe use of T/EMS
as a simulatorto explorehypotheses about the interactions betweenenvironmentaland
agent-structural characteristics. Weuse as an examplea question exploredby Burtonand Obel:is there a significant difference in performance
due to either the choiceof organizational
6?
structure or the decomposabilityof technology
Weequate a technologywith a TASMS
task structure, instead of a linear program.Taskstructures allow us to use a
clear interval measurefor decomposability,namelythe probability of a task interrelationship (in this exampleenables,
facilitates, and overlaps). Wedefine a nearly decomposable
task structure to havea baseprobabilityof 0.2 for these three
coordinationrelationships and a less decomposable
task structure to havea base probability of 0.8 (see Figure 2). Wewill
continuein this exampleto look at purely computationaltask
structures, althoughthe use of physical resourcescan also be
represented (see [Deckerand Lesser, 1994b]).
Burton and Obel were exploring the difference in M-form
(multidivisional) and U-form(unitary--functional) hierarchical structures; we will analyze the GPGP
family of teamoriented coordinationalgorithms. For our structural variable
we will vary the communicationof non-local views (GPGP
Module1). Informally, we will be contrasting the situation
where each agent makes commitments and communicates
results based only on local information(no non-local view)
with one wherethe agents freely share task structure information with one another across coordination relationships
(partial non-local view). Figure 3 showsan example--note
6 Technology
is usedhere in the management
sciencesenseof"the
physicalmethodby whichresourcesare convertedinto productsor
services" or a "meansfor doingwork"[Burtonand Obel, 1984;
Scott, 1987].
~
/
~
~
method
~
k minj~
~~
(executable
~ task
~
~ " "kmax]
~
task)
with quality
accrual function rain
1,~ enablesrelationship
....
facilitates relationship
~:-,
~~~
~
Less Decomposable
(p=0.8)
MoreDecomposable
(p=0.2)
Figure2: Example
of a randomly
generatedobjectivetask structure, generatedwiththe parameters
in the previoustable.
that in neither case does the agent have the global view of
Figure2.
Burtonand Obeluseda profitability indexas their performancemeasure,derived from the percentageof optimal profit
achieved. In general, the schedulingan arbitrary T/EMStask
structure is an NP-hardproblemand so we do not have access to optimal solutions. Instead we compareperformance
directly on four scales: the numberof communicationactions, the amountof time spent executingmethods,the final
quality achieved, and the termination time. Simulationruns
for each of the four combinationsof non-local view policy and level of task decomposabilitywere done in matched
sets--the randomlygenerated episode was the samefor each
combinationwith the exception of morecoordination relationships (including moreoverlappingmethods)being added
in the less decomposable
task structures. FollowingBurton
and Obel, we used the non-parametric Friedman two-way
analysis of variance by ranks test for our hypotheses. The
assumptions
of this test are that each block(in our case, randomlygenerated episode) is independentof the others and
that each block contains matchedobservations that maybe
ranked with respect to one another. The null hypothesis is
that the populationswithin each blockare identical.
Wegenerated 40 randomepisodes of a single task group,
each episodewas replicated for the four combinationsin each
block. Weused teamsconsisting of 5 agents; the other parameters used in generatingthe task structures are summarized
in
Table1 and a typical randomlygeneratedstructure is shown
in Figure2. Figure3 showsthe differencein the local viewof
one agent with and without creating partial non-localviews.
Wefirst tested twomajor hypotheses:
Hypothesis1: There is no difference in performancebetween
agentswith a partial non-localview andthose without. For
the communicationand method execution performance
measures,wereject the null hypothesisat the 0.001level.
Wecannotreject the null hypothesisthat there is no difference in final quality and termination time. Teamsofcom-
Parameter
Value
MeanBranchingfactor (Poisson)
MeanDepth(Poisson)
MeanDuration(exponential)
Redundant MethodQAF
Number
of task groups
TaskQAF
distribution
Decomposition
parameter
HardCRdistribution
Soft CRdistribution
1
3
10
Max
1
(50%rain) (50%max)
p = 0.2 or 0.8
(p enables)((l-p)
(p facilitates) (10%
hinders)
((.9-p) none)
P
Chanceof overlaps(binomial)
Table1: Parametersusedto generatethe 40 random
episodes
putational agents that exchangeinformation about their
private, local views consistently exchangemoremessages
(in this experiment,a meanincrease of 7 messages)but
less work(here, a meandecrease of 20 time units of work,
probably due mostly to avoiding redundancy).
Hypothesis2: Thereis no difference in performance
due to the
level of decomposabil#yof technology. For the communication and methodexecution performance measures, we
reject the null hypothesisat the 0.001 level. Wecannot
reject the null hypothesisthat there is no difference in
final quality and termination time. Teamsof computational agents, regardless of their policy on the exchange
of private, local information communicatemoremessages
(in this experiment, a meanincrease of 47 messages)and
do more work (here, a meanincrease of 24 time units)
when faced with less decomposablecomputational task
structures (technology).
Again following Burton and Obel, we next test for interaction effects betweennon-local view policy and level of
technologydecomposabilityby calculating the differences in
performanceat each level of decomposability,and then test-
36
ing across non-localviewpolicy. This test wassignificant at
the 0.05 level for communication,meaningthat the difference in the amountof communication
under the two policies
is itself different dependingon whethertask decomposability
is high or low. This difference is small (two communication
actions in this experiment)and was not verified in a second
independenttest.
5
NoA~J-Local
Vitwat AgentA (p*0.2)
Figure3: Example
of the local viewat AgentAwhenthe teamshares
privateinformationto create a partial non-localviewandwhenit
doesnot.
Conclusions & Future Directions
TA~MS
is a frameworkfor describing complextask environments. Whencombinedwith traditional DAItools for describing coordination algorithms and agents, it provides a
basis for analysis and/or simulationon either the TI Explorer
Lisp machine or the DECAlpha AXP.Whenanalyzing an
existing or proposedorganizational design or coordination
algorithm, we advocatean approachthat focuses first on behavior due to coordinationrelationships in certain situations
and then expandingthis modelto incorporate the sources of
uncertainty that are present. Sucha process mayiteratively
refine the organization or algorithm--with a parameterized
algorithm one might approach this as a pure parameter optimization problem.In [Deckerand Lesser, 1993b] we also
discuss howthe modelof performancein a particular episode
can be used with meta-level communication
to choosea good
organizational design dynamically wheninformation about
the current situation becomesavailable to the agents [Stinchcombe, 1990]. On the other hand, when trying to design
an organization or coordination algorithm for a given environment, one can also start with both the coordination
relationships and the uncertainties present, and add features
to deal with each explicitly (our implementationof the GPGP
algorithm, whichfeatures several independent’plug-in’ modules to deal withdifferent classes of interrelationships, is a
case in point). The two-level agent architecture that we use
with the GPGPfamily (with its separate coordination and
local schedulingfunctions) is not necessaryfor using TASMS,
whichviews agents only as consumersof subjective beliefs
and producersof actions.
Representing Organizational Roles
Future workwill include the analysis of moretraditional hierarchical organization forms. The addition of the concept
of ’organizational role’ can be accomplishedthroughthe use
of non-local commitments(and their accompanyingexpectations). In its simplestsense, agentA’s organizational
role is
a set of continuingnon-local commitments
to certain classes
of tasks. By making continuous commitmentsagents can
avoid communicatingabout every episode anew--assuming
the future structures are somewhatpredictable. This reasoning leads naturally to learning organizationalroles and to
open systems conceptsof temporarily settled questions (the
current set of continuous commitments)and algorithms to
re-open and re-settle these questionsas the task environment
changes. The developmentof such algorithms can then lead
to exploring conceptions of agents as nothing more than
convenient bundles of organizational roles [Gerson, 1976;
Gasser et al., 1989], and to whenand whyorganizations do
not act like ’big agents’ [Allison, 1971].Wewill also the expansionof commitments
to recta-level roles (i.e., comitments
to the coordinationparametersin effect for certain classes of
tasks).
The Tower of Babel
Upto this point, we have focusedon the interrelationships
and the uncertaintyarising directly fromthe generative model
of the environment,but wewouldalso like to explorethe uncertainty and variancearising fromthe differencein the objective and subjective models.Different relationships between
these modelswill lead to different organizationalstructures.
For example,in the case of uncertainty arising fromthe generative modelof an environment, we showedthat the wide
variance in performanceof a systemof agents with static organizationsin different episodesled to the use of recta-level
communication
to reorganize the agents to adapt to the particular episode at hand [Deckerand Lesser, 1993c]. Weare
also looking to expandthe T/EMS
conceptionof environments
to encompassmore dynamicsituations: another important
source of environmentaluncertainty. For example, in the
Towerof Babel problem[Ishida, 1992], agents try to pile
numberedblocks into a tower in numerical order. When
manyagents try to solve this problemwithout centralized
coordination, they tend to bottleneck aroundthe tower itself, bumpinginto one another. Ishida solves this problem
by dividing the agents into two groups:one to collect blocks
and bring them near (but not too near) the tower, and one
group to stack the tower. Fromour point of view, the task
structure at lowlevels is changingrapidly, and no agent can
(or needsto) keeptrack of all the changesin relationships
betweenagent and block positions. The rapid changein the
environmentmeansthat agents’ subjective views are always
out of date. By dividing the agents into two groups, the
bottleneck resource is controlled by organization--removing
it fromthe uncertainties facing the agents. Stackingagents
knowthere are not enoughof themto saturate the bottleneck, and other agents no longer use the resourceat all. The
uncertainty is still there, but it no longer has an impacton
the agents’ decision-makingprocess. A similar exampleis
Gasser and Ishida’s workon self-organizing productionsystems [Gasser and Ishida, 1991] where dynamicchanges in
the environmentallowed the systemto reorganize itself for
more efficient performance. Wecould potentially analyze
either of these systems.
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