From: AAAI Technical Report SS-98-05. Compilation copyright © 1998, AAAI (www.aaai.org). All rights reserved.
TowardUbiquitous Satisficing Agent Control *
Thomas Wagner and Victor Lesser
Computer Science Department
University of Massachusetts
Amherst, MA01003
Email: { wagner, lesser} @cs.umass.edu
Abstract
generally non-trivial, requiring either exponential workor,
in practice, a sophisticated solution schemethat controls the
algorithmic complexityand makesagent control a tractable
problem.
Our most recent work in agent control, namely Designto-Criteria
(Wagner, Garvey, & Lesser 1998; 1997a)
scheduling and GPGP(Decker 1995; Prasad & Lesser
1996) multi-agent coordination is directed at addressing
such agent control issues. Webelieve that the key to operating in these open environmentsis the ability to satisrice ubiquitously, and dynamically,to adjust problemsolving to the changing environmentand problem solving context. Oneview of this idea, that also appears in our work,
is the notion of resource-bounded-reasoning,i.e., solving
a problemin different amountsof time or with different
allotments of computationalresources, perhaps trading-off
solution quality and resource consumption. This is classically illustrated by an anytime (Zilberstein 1996) curve
and typically translates into parameterizingthe size of the
solution space that is searched during problemsolving (or
the level of abstraction at which problemsolving occurs).
However,this view is only part of the picture. Satisficing,
and the ability to satisfice, is intertwinedwith the ability to
approachproblemsolving froma flexible perspective, to attack problemsusing different techniques or using different
problem solving parameter settings, or consumingdifferent resources or quantities of resources. Flexibility enables
satisficing behavior. The existence of choices or alternatives begets the ability to performdifferently in different
circumstances- this is whythe ability to satisfice is important. In different circumstances, different problemsolving
behaviors are appropriate. The choice of whichsatisficing
behavior to employis situation specific, i.e., dependenton
the problemsolving context at hand. These three concepts,
satisficing, flexibility, and situation specificity are all interrelated and the boundarybetweenthese concepts blur when
they are examined closely. Through our work, we have
cometo see and partially understand the relationships between these concepts and the importance of addressing all
of these notions whenbuilding agent control systems. Un-
The dynamicsand unpredictability of open environments
pose a challenge to agent development.Agentsoperating
in these environmentsmustbe able to adapt processingto
the availableresourcesandthe currentproblemsolvingcontext. Theability to satisfice ubiquitously,in all aspectsof
agent problemsolving,is the key to existing in these environments.Our workon satisficing agent control problem
solving,i.e., multi-agentcoordinationandagentscheduling,
servesas the foundationfor our current focus ona holistic
approachto satisficing agentcontrol.
Introduction
With the advent of open computing environments adaptability in softwareapplications is critical. Since openenvironmentsare less predictable, applications must be able to
adapt their processing to the available resources (hardware,
software, time, money,databases, etc.) and the different
goal criteria set by different clients. In agent basedsystems,
where software applications are persistent, autonomous,
goal oriented, and function in real-time, this requirement
is doublyimportant. In this context agents must be able to
reason about their local problemsolving activities, interact
with other agents, plan a course of action, and carry out
the actions in the face of limited resources and uncertainty
about action outcomesand the actions of other agents, all
in real-time and within real resource and cost constraints.
Furthermore, in dynamicopen environments new tasks can
be generated by existing or new agents at any time, thus
an agent’s deliberation must be interleaved with execution
- rescheduling, replanning, and recoordination with other
agents is the norm, not the exception. To make matters
even more difficult the planning and scheduling tasks are
"Thismaterial is baseduponworksupportedby the National
Science Foundationunder Grant No. IRI-9523419and the Departmentof the Navyand Office of the Chief of NavalResearch,
underGrantNo.N00014-95i- 1198.Thecontent of the information doesnot necessarilyreflect the positionor the policyof the
Government
or the National ScienceFoundationand no official
endorsement
shouldbe inferred.
8O
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Figure 1 : T,’EMS
TaskStructure for GatheringAutoPurchaseInformation
derstandingthe full ramificationsof these conceptsis taking
the problemone step further. Wewill return to this issue in
the architecture section, but clearly satisficing on one aspect of problem solving normally has direct consequences
to other aspects of problemsolving, or problemsolving that
occurs downstreamtemporally.
fects), are also representedin T,’EMS
and the effects of the
interactions are reasonedabout statistically during scheduling and coordination. T/EMSmodels are the grounding
element and mediumof exchange for Design-to-Criteria
(Wagner, Garvey, & Lesser 1997a; 1997b) and Design-toTime (Garvey & Lesser 1995) scheduling, and and multiagent (Decker 1995) coordination research, and are being
used in Cooperative-Information-Gathering(Lesser et al.
1997), collaborative distributed design (Decker &Lesser
1995), distributed situation assessment (Carver &Lesser
1995), and surviveable systems (Vincent et al. 1998) research projects.
A simplified exampleof a Th~MS
task structure for gathering auto purchase information via the Webis shownin
Figure 1. The oval nodes are tasks and the square nodes
are methods. The top-level task is to Gather-PurchaseData-on-Nissan-Maximaand it has two subtasks GatherReviews and Find-Invoice-Price-Data. The top-level task
accumulatesquality according to the sum_all() quality accumulationfunction (qaf) 1 so both of its subtasks must be
performed to satisfy the objective. The Gather-Reviews
task has two methods, query Edmund’s-Reviewsand query
Heraud’s-Test-Drives. These methods are governed by a
sum() qaf thus the power-set of the methods minus the
emptyset maybe performedto achieve the tasks, i.e., Edmund’s maybe queried, Heraud’s maybe queried, or both
maybe queried. The Find-Invoice-Price-Datataskhas three
subtasks, two of type methodand one of type task, governed
by the max() qaf which is analogous to an ORrelationship.
Note the decompositionof the obtain invoice via AutoSite
task into two methods, one that locates the URLand one
that issues the query. The enables NLEbetween the URL
finding methodand the query method, in conjunction with
the low quality associated with the URLfinding method,
indicate that finding the URL
is necessary for task achievementbut that it contributes very little to achievingthe task
relative to the methodthat actually obtains the pricing report.
Before delving into the details of our satisficing agent
control work, let us ground further discussion in a highlevel overview of the modeling frameworkused in these
research projects. Our research focuses on a class of computational task structures wherethere are typically multiple
goals and multiple different actions for performinga particular task, whereeach action has different statistical performancecharacteristics, and the outcomesof actions are
highly uncertain. Wemodel these problem solving activities using the T,’EMS(Decker 1995) domain-independent
hierarchical task modeling framework. In T,’EMS,primitive actions, called methods,are modeledstatistically via
discrete probability distributions in three dimensions,quality, cost, and duration. Quality is a deliberately abstract
domain-independent
concept that describes the contribution
of a particular action to the overall problemsolving objective. Duration describes the amountof time that a method
will take to execute. Cost describes the financial or opportunity cost inherent in performing the action modeledby
the method.It is no accident that problemsolving activities
are modeledin these four (uncertainty via the probability
distributions) dimensions.Satisficing systems must operate
in multiple dimensionsto address the multi-dimensionality
of the environments in which they exist. Problemsolving
issues are not typically limited to time, other items are also
important, e.g., resources, financial costs, robustness, and
precision. Theability to adapt problemsolving for different
multi-dimensionalsets of goal criteria, amenableto future
extension,is critical.
Returning to T,’EMS,as with most hierarchical representations the high-level task is achieved by achieving some
combinationof its subtasks. Accordingly, since different
methodshavedifferent quality, cost, duration, and certainty
trade-offs, different solutions and partial solutions also have
different characteristics and different trade-offs. Hardand
soft interactions betweentasks, called NLEs(non-local ef-
1Qafsdefinehowa giventask is achievedthroughits subtasks
or methods.Thesum_edlO
qaf meansthat all of the subtasksmust
be performed
andthat the task’s qualityis a sumof the qualities
achievedby its subtasks.
81
ProblemSolver
(D~rnainPn~blernSolving)
Enumrratex I)omaiJ
andSy.vtrrn Con.vtraintx
DomainPlans *
Resource Constrainta *
Client Goal Criteria *
* Schedule Sdectedfi*r Execution
* Envelopc.~ Defining Ranges of Acceptable
Scheduler
( L~v.:alContru])
CommitraenL~
to Other Agents*
CiwarnitmenL~frum Other AgenL~*
CommitmentImportance Criteria
Ctamlination Module
(Non-ta~cal C(mccrns)
tives.
In our work, agents generally exist in a multi-agent environment and each agent consists of three primary components: a domainproblemsolver, a scheduler that determines
which domainactions to perform and in what sequence, and
a coordination modulethat coordinates interactions with
other agents. A high-level view of these three components
is shownin Figure 2. In these systems, the domainproblem solver describes domainproblemsolving options in the
T,’EMSlanguage and emits the generated task structures
along with goal criteria that specifies the desired quality,
cost, duration, and certainty of a path throughthe task structure that achieves the high-level task. Thescheduler inputs
the task structures and goal criteria and constructs a schedule customdesigned to achieve the task while adhering to
the criteria specified by the problemsolver. The coordination moduleworksin conjunction with the scheduler to handle interactions with other agents by giving and receiving
commitmentsto perform work at certain times. The importance of the non-local interactions is weighedagainst local
problem solving concerns during scheduling; the resulting
schedule is returned to the problemsolver for execution.
The schedule is annotated with performance envelopes and
monitoring points define whenrescheduling is necessary
due to unexpected (or unprobable) execution results. Note
that each agent has its ownlocal scheduler and local coordination module - agents are autonomousand there is
no centralized locale where all scheduling or coordination
takes place. Agents can behave in a more aggregate fashion, but this is through the use of organization rather than
fixed centralized control.
Specification of Local Pml,lem
Solving Actions Best Suited for thr
Current Lot~#l Context Mtululaled by the
Non-l~Jt’tdConlex!
]
Ramificationsof Lx,cal
Control to Non-Lt~’alContext
* Satisfied Commitments
\Data Flow
* Violated CommitmenL~
* ScheduleSelccW..dfi,r Execution
Figure 2: Key Agent Components
T/EMSmodels several important task features that facilitate a satisficing approach to problemsolving. The existence of alternative ways to perform tasks gives T~MS
based problem solvers (schedulers, coordination algorithms) options about howto achieve tasks. Another feature of the modelthat supportssatisficing is the statistical
characterizationsof primitive actions. Thedifferent statistical characteristics of alternative waysto achieve tasks gives
us the ability to explore different possible solution paths,
and consider the different trade-offs of each path, to find
one that best satisfices to meet the current problemsolving context. This is the T.’EMSscheduling problem. For
a given task structure and a given problemsolving context,
find a way(in real-time) to achieve the high level task that
is in keepingwith the context and the client’s resource constraints. Wewill discuss our Design-to-Criteria satisficing
scheduling workin more detail later in the paper. Modeling
task interactions or non-local-effects is also an important
feature of T?EMS.
Explicit representation of both the quality of interactions and the quantified ramificationsof interactions provides Th~MS
clients with information that can
be used to determine the relative importanceof workingto
resolve interactions. Froma scheduling perspective, this
meansreasoningabout the benefits of leveraging task interactions relative to other constraints and to alternative ways
of achieving the overall task. Froma coordination perspective, agents can reason about the benefts of coordinating
interactions with other agents versus the costs of such coordination. Wewill return to the issue of coordinationshortly.
Learningis also implicit in T/EMS.The probability distributions associated with primitive actions can be learned a
priori or be refined over time via learning. Domainindependence is also an important feature of T,’EMS.The expense
of customdesigning agent componentsis serving to inhibit
widespread agent development; moving toward a flexible
and adaptable domainindependent agent control system is
critical for the future of agent systems.As wediscuss in the
architecture section, integrating domainindependentagent
control componentsis one of our primary near-term objec-
Design-to-Criteria
Scheduling
The scheduler is the heart of satisficing agent control. An
agent equipped with a Design-to-Criteria scheduler is able
to adjust its problemsolving activities to meet changesin
resource requirements and to reason about the trade-offs of
different courses of action. The central objective in Designto-Criteria scheduling is to cope with the combinatorialexplosion of possibilities while reasoning about a particular
set of client goal criteria and the trade-offs presented by
different solutions and partial solutions. In other words,
schedulerclient applications or users specify the designcriteria and the scheduler designs a schedule to best meet the
criteria, if possible given the task model. Figure 3 shows
a set of satisficing schedules producedby the Design-toCriteria scheduler, for the sampletask structure (Figure 1),
using four different sets of design criteria. ScheduleA is
constructed for a client interested in a fast, free, solution
with any non-zero quality. Schedule B suits a conservative
client whois interested primarily in certainty about quality
achievement.Schedule C is designed for a client whowants
high quality information, is willing to wait a long time for
82
ScheduleA: Fast and Free
[Edmund’s-Reviews[
Edmund’s-Price-Guide
I
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0)(5%
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124,270)(6%
ExpectedQ: 21
Q Certainty: 93%
ExpectedC: 0
C Certainty: 100%
ExpectedD: 255 scconds
D Certainty: 50%
SchcduleC: G~x,dQuality, M~vJerate
Cost, Slow
IEdmund’s-ReviewslHeraud’s-Te.st-Drives
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ExpectedQ: 40
Q Certainty: 78%
ExpectedC: $4.95
C Certainty: 1(10%
ExpectedD: 920 seconds
D Certainty: 70%
ScheduleB: HiJ/h QualityCertainty,MtnlerateCost
lEdmund’s.Reviewsilntelichoice
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ExpectedQ: 26
Q Certainty: 98%
ExpectedC: .$4.95
C Certainty: 100%
Expected D: 647 ~conds
D Certainty: 50%
ScheduleD: HighQuality, HighCost, ModerateDuration
[Edmund’s-Revicws]Heraud’s-Test-Drives]Get.AutoSite.URLllssue.AutoSite.Request
]
Q (1%0)(44, 27)(19%34)(24, 41)(74%
C (1004,
$9.95)
D (20%630)(31%
690)(24%
72(l)(19%
740)(6%
ExpectedQ: 46
Q Certainty: 74%
ExpectedC: $9.95
C Certainty: I(X)%
ExpectedD: 698 seconds
D Certainty:514,
Figure 3: FourSatisficing Schedules
an answer, and is only willing to spend $5 on the search.
Schedule D is meets the criteria of a client whowants the
highest possible quality, is willing to spend $10, and wants
the gathered data in 15 minutesor less.
The fundamental premise of our scheduling work is that
the goodnessof a particular solution is entirely dependent
on a particular client’s complexobjectives and that different
client’s have varying objectives. Thusthe scheduling process must not only consider the attribute trade-offs of different solutions, but must also do so dynamically.Furthermore, the scheduling process must be efficient - because
of the inherent uncertainty in the domains, where actions
mayfail or have unexpectedresults, scheduling activities
are interleaved with planning and execution. Thus scheduler inefficiencies are multiplied manytimes during a problem solving instance.
In general the upper-bound on the number of possible schedules for a task structure containing m methods is ~--~ir=~ o (r~)i! and the w(2m) and o(mTM) combinatorics of our scheduling problemprecludes using exhaustive
search techniques for finding optimal schedules. Design-toCriteria copes with these explosive combinatoricsby satisricing with respect to the goal criteria and with respect to
searching the solution space. This satisficing dualismtranslates into four different techniquesthat Design-to-Criteria
uses to reduce the search space and makethe scheduling
problemtractable:
mitmentsmadewith other agents and so forth.
Heuristic Error Correction The use of approximation
and heuristic decision makinghas a price - it is possible to create schedules that do not achieve the high-level
task, or, achieve it poorly. A secondary set of improvementheuristics act as a safety net to catch the errors that
maybe correctable.
Design-to-Criteria thus copes with computational complexity by using the client goal criteria to focus processing,
reasoning with schedule approximations rather than complete schedules, and using a heuristic, rather than exhaustive, scheduling approach. This methodologyis effective
because several aspects of the scheduling problemare soft
and amenableto a satisficing approach. For example, portions of the client goal specification (Wagner, Garvey,
Lesser 1997a) express soft client objectives or soft constraints. Solutions often do not need to meet absolute requirements because clients cannot knowa priori what types
of solutions are possible for a given task structure due to the
combinatorics.Similarly, soft task interactions also represent soft constraints that can be relaxed, i.e., they can be
leveraged or not dependingon the situation. Finally, though
the TJ~MSscheduling problem is more complex than many
traditional scheduling problemsbecause of its representation of multiple approachesfor task achievement,it is also
moreflexible. If we viewthe schedulingactivity as a search
process, typically there is a neighborhoodof solutions that
will meet the client’s goal criteria and the lack of exhaustive search, i.e., search by focused processing and approximation, does not necessitate scheduling failure. Several
illustrations of Design-to-Criteria at work, and morealgorithmic details, can be found in (Wagner,Garvey, &Lesser
1998).
Criteria-Directed FocusingThe client’s goal criteria is
not simply used to select the "best" schedule for execution, but is also leveragedto focus all processingactivities on producingsolutions and partial solutions that are
most likely to meet the trade-offs and limits/thresholds
definedby the criteria.
ApproximationSchedule approximations, called alternatives, are used to provide an inexpensive, but coarse,
overviewof the schedule solution space.
Heuristic Decision MakingThe action ordering scheduling problem also suffers from large combinatorics. We
cope with this complexityusing a group of heuristics for
action ordering. The heuristics take into consideration
task interactions, deadlines, resource constraints, com-
GPGPMulti-Agent Coordination
Satisficing in the GPGP(generalized partial global planning) multi-agent coordination workto date takes a different from than satisficing in Design-to-Criteria scheduling.
In coordination, flexibility with respect to computational
resources is derived from a modularizedcoordination approach that enables different modules,with different over-
83
head costs, to be applied independently from one another.
Whenresources are tight, only the most rudimentary coordination is used. Whenresources are less scarce, or the
beneft of coordinating outweighs the cost of the coordination overhead, agents coordinate over "optional" interactions like soft interactions in T/EMS.The GPGPmodularized coordination approachfacilities a range of cost/benefit
options for any problemsolving episode as different subsets
of coordination modulesmaybe applied.
All coordination modules in GPGPcoordinate through
the use of commitments,
that is inter-agent contracts to perform certain tasks by certain times. Commitments
are made
through the coordination mechanismsand considered by
the scheduler whenthe local course of action is decided.
GPGPdefines the following coordination mechanisms(for
the formal details see (Decker1995)):
Share Non-LocalViews This most basic coordination
mechanismhandles the exchangeof local views between
agents and the detection of task interactions.
CommunicateResults This coordination mechanismhandles communicatingthe results of methodexecution to
other agents, at various levels of detail and coverageas
defined by different communication
policies.
Avoid Redundancy This mechanism deals with detected
redundancyby committingan agent at randomto execute
the redundant methodin question.
Handle Hard Task Relationships - The enables NLE
pictured in Figure 1 denotes a hard task relationship.
This coordination mechanismdeals with such hard, nonoptional, task interactions by committingthe predecessors of the enables to performthe task by a certain deadline.
Handle Soft Task Relationships Soft task interactions,
unlike hard interactions like enables, are optional. When
employed,this coordination mechanismattempts to form
commitments
on the predecessors of the soft interactions
to performthe methodsin question by a certain deadline.
As mentioned above, the GPGPcoordination module
modulates local control by placing constraints, commitments, on the local scheduler. The commitmentsgenerally
fall into three categories: 1) deadline commitments
are contracts to performworkby a certain deadline, 2) earliest start
time commitmentsare agreements to hold-off on performing certain tasks until a certain time has passed, and 3) do
commitmentsare agreements to perform certain tasks without a particular time constraint.
GPGPachieves flexibility through a modularized approach to coordination. The question then becomeswhich
combinations of mechanismsare most effective. Empirical
analysis (Decker 1995) has shownthat no single coordination mechanism,or sets of mechanisms,is most effective
for all situations. Instead, as shownin Prassad and Lesser
84
(Prasad & Lesser 1996), coordination is best viewedas
situation specific activity; whichset of mechanisms
is most
effective is dependenton the types of tasks in question and
the overhead costs of communications.In different environments, different mechanismsare most effective.
The Future: A Holistic Satisficing
Architecture
Agent
Both GPGPand Design-to-Criteria are able to adapt processing to a given situation. GPGP,uses a modularizedapproach to provide different degrees of coordination, each
with its ownoverhead costs and benefits to problemsolving. Design-to-Criteria uses a satisficing methodologyto
control the combinatoricsand to produceschedules in realtime. This illustrates an important point; satisficing can
take manyforms, e.g., performingless search (Design-toCriteria), or using less context to makedecisions (GPGP),
or working from default assumptions (situation specific
GPGP).In fact, the implementationof GPGP
is itself an illustration of satisficing by sacrificing context. GPGP
forms
commitmentsbetween agents through a one-shot mechanism where two agents form an agreement about task performanceindependently of the other agents in the system.
This mechanismreduces the overhead required for coordination at the expense of making the commitmentswithout
understanding more of the group context. In contrast, if
the commitmentforming context is broadened to include a
groupof problemsolving agents, to support a multi-step negotiation process, the overhead of commitmentformulation
will increase but so will the amountof information used to
make commitmentdecisions.
In addition to meetingdeadlines, Design-to-Criteria also
builds customschedulesto suit a particular client’s multidimensional goal criteria. This latter Design-to-Criteria
feature represents one next step in the evolution of GPGP
and our multi-agent coordination work. Currently, though
GPGPprovides a modularized toolbox of coordination
mechanisms, it does not reason about which mechanisms
to use to meeta particular client’s needs(or goal criteria).
The workin learning which coordination algorithms to use
in particular situations is a step in the right direction, but
this work also does not address meeting dynamic multidimensional goal criteria. Like Design-to-Criteria, GPGP
mustbe able to adapt its processingautomaticallyto a given
problemsolving context and resource constraints.
Despite Design-to-Criteria’s strengths, neither aspect of
our agent control work is complete. Neither system accounts for the actual cost of the control problemsolving activity. In other words, thoughthe scheduler copeswith hard
combinatorics to produce schedules in interactive time, it
does not account for the resource costs of the scheduling
processitself, i.e., Design-to-Criteriais fast, but it doesn’t
reason about the time required to find a good schedule and
accountfor this time, and adjust its activities accordingly.
Throughthe focusing mechanismit can adjust its ownproblem solving to different resource requirements, but right
now,the focus is definedby the client or hardwireddefaults.
The right approach for coordination and scheduling, and
overall agent control, is to accountfor the both the control
and domainproblemsolving costs, to reason about resource
allocations and the cost/benefits of such allocations, and
to control the agent accordingly. The benefits of spending
time doing further coordination or morerefined scheduling
versus time spent problem solving must all be compared
and reasoned about. This meta analysis (Russell & Wefald
1991) approachto agent control in conjunctionwith the satisficing abilities of Design-to-Criteriaand GPGP,
will result
in agents beingable to satisfice all activities and adapt to a
wide range of dynamicproblem solving environments.
However,this is an oversimplification of the problem.It
is importantto note that the issue is not to simplydetermine
a resource allocation to control and domainproblemsolving, but to grapple with the downstream
ramifications of the
allocations and satisficing behaviors employedor considered. Control problemsolving and domainproblemsolving
are interdependentactivities. Considera case wheretime is
allocated betweena domainproblemsolver and a scheduler,
and the domainproblemsolver faithfully describes its problem solving options in a T/EMS
task structure and then asks
the scheduler to determinea course of action. If the scheduler satisfices, to meetits time resourceconstraint, it will
do so by exploring a smaller part of the schedule solution
space. Onepossible outcomeof this is that the scheduler
will fail to generate a very goodsolution and will instead
generate and return a mediocresolution. The domainproblem solver will then execute the schedule and obtain lower
quality results than it mighthaveif less time had beenallocated to domain problem solving and more time had been
allocated to scheduling. In this example, the downstream
effect of control satisficing wasnot generatinga goodresult
in the time alloted. In other cases, the ramifications range
fromthe introduction of additional uncertainty to being unable to find any solution because one of the components
was over constrained.
The previous exampleis cast in terms of time allocations,
but as mentionedearlier, satisficing can be in manydimensions like precision, robustness, and cost, and it can be in
terms of multiple dimensions simultaneously. This is what
motivated our recent workin multi-dimensional goal criteria for Design-to-Criteria scheduling (Wagner, Garvey,
Lesser 1997a). But even that work needs to be pushed to
another level - satisficing to meeta particular set of multidimensional goal criteria is only part of the problem. In
most problemsolving situations, there are actually multiple sets of goal criteria, i.e., there is no single evaluation
function. Considera case wherea client tells the scheduler
85
to producea schedule that will achieve the task while minimizing time and incurring no cost. If, for the given task
structure, staying within these constraints meansgenerating a very poor result, the client maywant to modifythe
constraints and ask for another schedule, rather than execute the schedulethat was returned for the first set of goal
criteria. Agent control components,and the holistic agent
control mechanism,must reason about multiple sets of goal
criteria, or provide a meansfor negotiation or iterative refinementto generate a set of criteria that is suited for the
problemsolving context and the task at hand.
Giventhe complexissues surrounding the issue of satisricing, our goal of a satisficing domain-independent
agent
control architecture seems quite ambitious. The satisficing cohesion neededfor this architecture will require that
all componentshave the ability to meta analyze their problem solving activities and to estimate the local (as everything is intertwined) costs/benefits of resource allocations.
Fromthe scheduling and coordination perspective, this is
an achievable objective. For the domainproblem solver,
meeting this requirement is application dependent. However, to the extent that the domainproblemsolver can enumerate its problemsolving options as a T/EMStask structure, the scheduler can then reason about the domainproblemsolver’s activities explicitly. In fact, we maymovetowardrepresenting all major control and domainactivities in
T/EMStask structures, and even estimating and modeling
(via nles) the downstreaminteractions of resource allocations, and analyzing it using the Design-to-Criteria scheduler. If this approachis used, wemust then also accountfor
the time required to perform the meta-analysis. However,
for certain classes of task structures the schedulinganalysis
time is highly predictable. For large problemsolving tasks,
we also plan to explore changing the problemsolving horizon, the distance betweenthe current point in time and a
point at whichallocations will be reexamined.
Satisficing, flexibility, and multi-dimensionaltrade-off
behavior, are critical for agents in complexdynamicopen
environments. Our future work is centered on a holistic,
domainindependent, agent control architecture designed to
achieve these goals. It is our hopethat with the architecture
agent developers will be able wrap or embedtheir domain
problemsolvers and create satisficing and flexible multiagent systems.
Acknowledgments
Wewouldlike to thank Professor Keith Decker, Professor
Alan Garvey, Professor NormanCarver, and Dr. Nagendra
Prassad, for their contributions to this ongoingwork.
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