From: AAAI Technical Report WS-97-05. Compilation copyright © 1997, AAAI (www.aaai.org). All rights reserved.
Constraints and Agents in MADEsmart
Leo Obrst
Boeing Defense & Space Group
Helicopters Division
Advanced Computing Technologies
P.O. Box 16858, MSP29-99
Philadelphia, PA19142-0858
leo.obrst~boeing.com
intelligent agent framework(Decker 1995; Decker, Lesser,
Nagendra Prasad, & Wagner 1995; Williamson, Decker &
Sycara 1996; Decker & Sycara 1996). Although the project
is currently only in an early stage of development, this
framework is expected to employ a constraint-centered
System Design Management Agent [SDMA]developed by
the University of Toronto’s IE Department in conjunction
with Boeing (Bilgic et al. 1996; Bilgic, 1997). The SDMA
will use the constraint-based Toronto Ontologies for a
Virtual Enterprise (TOVE)ontologies (Gruninger &
1995), and its domaintheories for design engineering (Lin,
Fox, & Bilgic 1996) and dependent underlying theories,
phrased as KIF/Ontolingua assertions (Genesereth &Fikes
1992; Gruber 1993; Gruber 1994, Gruber & Olsen 1994) in
an axiomatic system running in the constraint logic system
ECLiPSe(Meier 1995), as its primary knowledgeresource
to monitor an ongoing design project, offering resourceallocation, coordination-strategic,
and risk-abatement
suggestions to the interacting agents and humansas best it
can. In addition, the underlying domainmodel and agent
architecture
for MADEsmartwill employ primitive
constraint notions to facilitate the design engineering
process. These constraints will be evaluated by a given
agent at goal commitment time, during planning,
scheduling, or execution of tasks.
Abstract
As part of the DARPA
Rapid Design Exploration and
Optimization(RaDEO)
program,Boeing, Philadelphia,
involved in an on-going concurrent design engineering
research project called MADEsmart
whichseeks to partially
automatethe Integrated ProductTeam(IFr) conceptused
Boeingfor organizingthe designengineeringprocess, with
the aid of intelligent agent technology.Althoughcurrently
only in an early stage of development, the project is
expectedto crucially employa constraint-centered System
DesignManagement
Agentdeveloped by the University of
Toronto’s IE Departmentin conjunction with Boeing. The
SDMA
will use the constraint-based TorontoOntologiesfor
a Virtual Enterprise (TOVE)
ontologies, and its domain
theories for design engineeringand dependentunderlying
theories, phrased as KIF/Ontolinguaassertions in an
axiomatic systemrunning in the constraint logic system
ECLiPSe,as its primary knowledgeresource to monitoran
ongoingdesign project, offering resource-allocation,
coordination-strategic,andrisk-abatementsuggestionsto the
interactingagents as best it can. In addition,the underlying
domainmodeland agent architecture for MADEsmart
will
employprimitive constraint notions to facilitate the design
engineeringprocess. Theseconstraints will be evaluatedby
a given agent at goal commitment
time, during planning,
scheduling,or executionof tasks. This paper describesthe
expected interaction of constraints and agents of this
ongoingresearch.
Agents, Agent Infrastructure,
and Constraints
The current modelof an individual agent in MADEsmart
is
displayed in Figure 1, and is described below. This
TAEMS-centricview of the agent considers an intelligent
agent as communicating with other agents -- in
MADEsmart,
using a version of KQML
(Finin et al. 1993)
-- and being locally responsible for the planning,
scheduling, and execution of task structures, though those
tasks mayhave non-local effects on other agents or be nonlocally affected by other agents. MADEsmartdiverges
somewhatfrom TAEMS
in that the system is intended to be
an actual, not just simulated, environment for agent
interaction with humansand other software systems.
A task structure in our view is a hierarchy of subtasks
which need to be reduced and eventually instantiated by an
Introduction
As part of the DARPARapid Design Exploration and
Optimization (RaDEO)program, Boeing, Philadelphia,
involved in an on-going concurrent design engineering
research project called MADEsmart
(Barrett et al. 1997;
Obrst et al. 1997) which seeks to partially automate the
Integrated Product Team(IPT) concept used by Boeing for
organizing the design engineering process, with the aid of
intelligent agents operating in a blackboard environment
(Lander, Staley & CorkiU1996). The core architecture
MADEsmart is centered
on the Task Analysis,
Environment Modeling, and Simulation [TAEMS]-related
83
agent based uponan internalized notion of a goal to be
satisfied. Thisis also similarto the unifiedreactive/rational
conceptionof agents of (Kowalski&Sadri 1996), whonote
correlations to deductivedatabase techniques(Das 1992),
and wewill investigate applyingtheir proof procedurefor
the agent control cycle (using goal reduction rules and
integrity constraints) to the TAEMS
model.In our view, an
agent receives a KQML
request from another agent to
achievea particular goal. This interaction is displayedat
the top of Figure1, whichalso depicts the requestingagent
interacting with events on the blackboard,a representation
of the evolving implementationof the agent environment.
Therequest also has somecontentassociatedwith it, in the
formof constraints andpossiblyother assertions related to
the goal, phrased in a K1F-subset(Genesereth & Fikes
1992) language.
MADEsmart
AgentInternal
Figure 1. MADEsmart
Agent Internal View
The receiving agent first determines whether it can
possiblysatisfy the goal -- i.e., does it possessanytask
structures (hierarchically composed
of subtasksin various
ordering relationships, with actions as leaves) - by
consulting a rule base of goal-to-task mappingsin its
internal knowledgebase, and committingif it determines
that it can satisfy the goal request. Oncethe agentcommits
to the goal, it continuesby planningwhichtasks to perform,
i.e., a reduction andthen instantiation processwhichmay
evaluate, with respect to the tasks, certain domain
constraints fromthe contentslot of the original request. In
Figure1, the constraintflowis representedby a dottedline.
In general, wewishto evaluate only domainconstraints
in the planning process, and delay the evaluation of
performance
constraints until the schedulingand execution
84
phase.Becausethe project is still in its infancy,however,
weare unsure what constitutes such clearly delineated
domainconstraints. It maybe the case, for example,that
there are twoalternative task structures to performa given
type of analysis, say a stress analysis of a proposeddesign
for an aircraft wingpanel, andthe constraint tradeoff is
formulatedin terms of granularity of the analysis vs. the
time requiredto performthe analysis. Agross analysis will
take less time than a finer analysis. It maybe that an agent
can makethat decisionearly in the planningprocess, rather
than delayit for the schedulingprocess, whereperformance
constraintsare evaluated.
After planningis completed,the agent locally schedules
(in cooperation with a scheduling module which has
awarenessof other agents’ tasks) and executes the task
structures whichwill satisfy that goal, interacting with
other agents during this process if there are non-local
effects. Appropriateconstraints maybe evaluated at both
scheduling and execution times. As mentionedearlier,
these constraints in generalwill be performance
constraints
and concern notions such as application resources, time
(duration, deadline), and quality (a vector of metrics
task evaluation).
Part of the interaction among
agentswill be to coordinate
the use of software and hardware applications which
facilitate the designandanalysis activities of the design
engineeringprocess. Theseinclude both CAD
tools, which
contain geometricand parametric data on the design, and
analysis tools, most of which are legacy applications
performingevaluation or optimization of factors such as
weight, stress, and producibility. Althoughour initial
modelof the type of schedulerwewill employis that most
closely correlated to the TAEMS
architecture i.e., Designto-Time (Garvey & Lesser 1993) and Design-to-Criteria
(Wagner,Lesser, &Garvey1996),wewill also try to link
to the constraint-reasoning SDMA,
whichwill embodya
risk assessor embodiedas a Bayesian network. The SDMA
will inspect agents’ rules andtask structure libraries, in
addition to communicating
directly with the agent, to offer
it advice during the planning and scheduling processes.
Anotherkind of agent interaction proposedinvolves the
SDMA
interacting with the agent environmentconcerning
moreglobal notions about the ongoingdesignprocess, such
as risk-abatement,coordinationstrategies, and resourceallocation schemes, and inspecting the internal data
structures of the evolvingdesign network.Finally, other
kinds of interaction will facilitate humanand agent
collaboration.
Figure 2 displays a picture of the proposedMADEsmart
agent interaction process. In this schema,each user is
associated with a User CoordinationAssistant Agent, an
agent whichrepresents the user’s interface to the system,
andhis/her interests, views,status, etc., withrespectto ongoing designs and projects. User modelsmayor maynot
be globally available (displayed as the upper leftmost
knowledge
store in Figure2), but will be accessible to the
UCAA.
The UCAA
agents will initially perform the tasks
specifiedby the user, but it is anticipatedthat eventually
dedicated agents representing functional responsibilities
such as stress analysis or organizational entities will be
necessary. These are displayed as specialized agents
intervening
between a given UCAAand individual
Custodian/Design Thread agents. These latter agents are
specialized information agents who maintain a given
project’s
evolving design network, an internal
representation of connected nodes which contain at least
some information on the applicable constraints holding at
correlated temporal points in the design evolution and
partly representing design alternatives.
Another aspect of the interaction between agents and
constraints in MADEsmart
is in our use of the cost model
COSTADE
(Mabson et al. 1996), to drive design analysis
(including producibility, stress, and weights analysis),
Multi-Disciplinary Optimization, and preliminary and
detaiIed design. COSTADE
is a Fortran-based model we
maywish to partially disentangle and map componentsto
the constraint logic-based SDMA.
contents of this paper remains speculative in nature, hedged
with anticipations. The work envisioned is collaborative,
and we expect additional crucial contributions from our
subcontractors.
Although the primary purpose of
MADEsmartis to enable agents to facilitate
the
collaborative design process amonghumans, we have hope
that they may fill a larger capacity, as intelligent
collaborators themselves working on a design project
represented as a networkof constrained design alternatives.
Acknowledgements
This work is supported by cooperative agreement
70NANB6H0074
under the DARPARaDEOprogram, with
the majority of the funding from DARPA(with NIST
participating and acting as their agent) and the remaining
funding supplied by Boeing.
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