One possible role for machine learning in collaborative
design (a few preliminary thoughts)
From: AAAI Technical Report WS-93-07. Compilation copyright © 1993, AAAI (www.aaai.org). All rights reserved.
N. Ivezic 1, J. Garrett I ~
and D. Shaw
~Department
of Civil Engineering
CarnegieMellonUniversity
Pittsburgh PA 15213-3890 USA
I
7Division
of InfrastructureFacilities
USArmyConstructionEngineeringResearchLabs
POBox 9005
Champaign IL 61826-9005 USA
Introduction and Motivation
As the nameimplies, collaborative design is a process performedby manydesigners. Each designer makes
a subset of the decisionscomprisinga design andmustmodifyhis or her decisions whenthey adverselyinfluence someaspect of the design. To complicatethe task, each designer is equippedwith his or her specific
schemaor world-viewthat makescommunicationbetween them difficult. Definitions of commonlyused
objects can serve as examplesof this difficulty. Movable
partitions maybe seen as "walls" by space planners
but wouldnot fit into that categoryfromthe viewpointof structural designers. Othersmaythink of the word
"wall" as associated with amountsand color of paint while a computergraphics specialist maybe moreconcemedwith the "two sidedness" of the "wall" object for manipulationpurposes. Theattribute of two sides
has external andimemalimplications whenrelated to fire codes, insulation, or covering.
Duringthe collaborative design process, the individual designers makedecisions about the design basedon
a set of objectives that are importantfromtheir perspective and for whichthey are preparedto design and
evaluate their designs. Manyof these decisions mayhave several altematives fromwhichthe designer can
select and that havedifferent influences on designdecisions already made,and yet to be made,by other designers with other objectives. However,oftentimes the designer makinga decision is unawareof, or unable
to determine,howhis or her decision will impactthese other designobjectives.
Oneapproachto addressingthis problemis to provide the designers with "designadvisors" that observewhal
the designcris doingand attemptto predict whatinfluences, if any, the current decisionsbeing madewill have
on other designers and design objectives. Designadvisors havebeenintroduced, althoughsomewhat
vaguely,
in the framework
of concurrentdesignin several places [Finger 92, Pohi 92]. In this paper wedefine design
advisors as agents capable of supportingdecision makingprocess pro-actively, rather than reactively as design critics. For example,if a designer had a designadvisor that could support the decision makingprocess
with respect to the ease or difficulty of disassemblyof componentsforminga design, that design advisor
wouldbe able to give advice about whichdecisions amongthe multiplicity of altematives wouldlead to a
gooddesign product with respect to product disassemblyand recycling. Hence,design advisors merelyprovide adviceto designers or, equivalently, designagents who,in tum, are solely responsiblefor the decision
makingprocess.
Developmentof these design advisors in a nonautomaticmannerusing manualknowledgeacquisition and
modelingmethodsmaybe prohibitively expensive. However,using machineleaming, it should be possible
to acquire these influences frompast designexperienceand to use this knowledgeto determinethe adverse
influences that a current set of designdecisions mayhaveon someset of importantaspects of the product,
either not familiar to the designeror seen in a different schema.This paperwill discussthe role that machine
Icamingcan play ill developingthcse dcsign advisors and will outline a commctionistapproachto acquiring
and representing this knowledge.
2
A Methodology: Adaptive Design Advisors for Collaborative
Design
In this section weproposea methodology
to developan adaptive designadvisor framework
for collaborative
design. The following are the assumptionson whichthe methodologyis based:
¯ Early stages in designare critical decision makingstages. The early design stages needto accountfor
downstream
considerations of design, manufacturing,construction, maintenance,and disassemblyof the
design product.
241
¯
Early design decisions are abstracted informationabout the design process. Designdecisions that are
madein the early design stages together with their downstream
influences needto be representedin an
abstract wayto allow for learning and reasoningabout the design objectives.
Thedesignpr~cessenc~mpassesmultipleperspectives.Ineachdesignstage~di~erent~bjectivesf~r~and
perspectives of, adesign productofferanatural"clustering"ofdesigndecisions.Decisions within acluster haverelatively scarce interaction with decisionsin other clusters, compared
to the decisionswithinthe
cluster. However,
these "inter-cluster" interactions are of fundamentalimportancefor the quality of design product. There are one or moredesign agents that are responsible for the decision makingprocess
delegatedto each such cluster.
¯
The dependencystructure in the decision makingprocess is identifiable. Thedependenciesbetweendifferent designdecisions can be established throughthe cooperativeprocessof identification, dialog, and
negotiation amongintelligent agents.
¯
Different perspectives require different representationsof design decisions. Designdecisions that are
madeby different agents contain informationwhichare selectively used by other agents.
¯
Evaluativeknowledgeexists and is usableto guide the synthesis process. This is the key assumptionlbr
enabling a machinelearning approachemployedin the proposedcollaborative framework.This assumption leads to a requirementthat the machinelearning approachmustbe capableof capturing the"inverse"
of the evaluative knowledge.In other words, the learning approachmust construct mappingsfrom the
desiredfunctionalityto the formof the artifact that satisfies this desiredfunctionalityby observingresults
of past design processes. Theconnectionist approachreported in [Ivezic 92] meets this requirement.
A methodology
for building an adaptive design advisor enviro~mlentis given and illustraled by an example
fronl [Pohi, 19921. Thefollowingare the steps involved in the methodology:
¯ Identification ofmultiple designperspectives.Different designperspectivesare usuallyeasily identified.
Theseperspectives haveassociated a group of decisions that are tightly interrelated amongeach other.
A relatively large numberof perspectives are identifiable in the architectural building design domain.
Someof these perspectives are noise isolation, construction, space adjacency,thermal, and structural.
Evenwithin these globally identifiable perspectives, there are viewsthat observedifferent parts of the
domain.For example,structural considerationsthat are concernedwith seismicreliability of a residential
buildingare concernedwith horizontal slabs only to the extent to whichthey providesufficient stiffness
to ignore the differential horizontal deflections betweenvertical elements.
¯
Identification of design decisions andtheir dependencies.Eachdesignadvisor deals with a multiplicity
of designdecisions that together represent a completesolution of the designproduct fromthis advisor’s
perspective. For each designdecision there needto be identified dependentdesigndecisions both within
the domainof the advisor and outside the domain.
Considertwoperspectivesof an architectural designof residential buildings: structural and thermalperspectives. Struclural considerationincludes selection of the roof systemfor the building. Thermalconsideration includes the management
of the temperaturefluctuations in the interior of the building. Given
the large diurnal temperaturespans, thermal analysts mayrequire large structural massin order to keep
the temperaturefluctuations low. This requirementis not typically consideredby the structural designer
andit will interfere with the decisionsto select a light roof system.
¯
Negotiationof the interaction languagebetweendesignadvisors. For each external design decision that
influencesits decision makingprocess, the designadvisor negotiates with the designadvisor whichoriginates the decision about the content of the informationrequired for its owndecision makingprocess. A
lemplatefor the informationrequestedfromthe other advisoris establishedas an outcomeof this negoti-
242
ation and both the requestor and providerof informationare awareof informationlink. Since the dependenciesare bidirectional, the two interacting advisors mayeach play both requestor and provider role.
In the given example,the thermalagent will initiate the negotiation about the kind of informationthat
is neededfromthe structural advisor. Thethermalagent mayrequire the structural agentto makeaccessible the decisionsaboutthe roof systemand the vertical structural system.Thethermalagent may,in addilion, require a particular piece of informationwhichwill closely describethe effect of the structural decisions on its ownactions, for example,thicknessandmaterialof the structural walls. Conversely,the structural advisor mayrequire informationabout the required structural massfromthe thermaladvisor to determine howto distribute the required massamongthe structural elements.
¯
Establishingrepresentationsfor designdecisions. Designdecisions shall be representeddifferently by
different designadvisors reflecting the informationcontent importantfor the decision makingprocess of
eachadvisor. For example,while the exact widthof the wall is importantfor the acoustic considerations,
this informationis irrelevant for the adjacencyconsiderations.
¯
Establishingthe controlstrategy for the designprocess. Designadvisors are intendedto supportthe decision makingprocess by providingadvice to a design agent about the consequencesof its decisions with
respect to other designconsiderations.Hence,each advisoris capableof makingdecisionsthat satisfy the
global design constraints imposedon the design decisions outside of the domainof the designagent. In
the case of the global conflicts betweendesignagents, a needfor an agent to resolve the conflict arises.
in our example,the control of the designprocessmaybe distributed to designagents that locally receive
the advice about the goodand the bad design decisions. However,whenthe conflict occurs betweenthe
thermaland structural agents with respect to the "alternative choicesfor the roof system,a designagent
needsto makea decision basedon the global utility of the two alternatives. In this case, there exists a
needfor the global decision makerto resolve any conflicts accordingto the globally preferred solution.
Still, existence of such a global decision makerdoesn’t restrict the remainingdesignagents with advice
fromdesign advisors to makedecisions whichdo not result in a global conflict.
3
A Connectionist Approach to Adaptive Design Advisors
Weproposea connectionist approachto implementthese adaptive design advisors based on the methodology
developedin [Ivezic 92]. The connectionist approachemploysBayesian Belief Networks[Pearl 1988] to
capture the dependencystructure and prior knowledgeabout the domain,whichis then refined throughthe
learning process. Thefollowingstages are involvedin building these connectionist design advisors:
¯ Elicitation of dependencystructure from humanexperts for a given design problem.Create a Bayesian
Belief Network
that reflects the elicited causal knowledge
[Pearl 88]. If possible, elicit transition probabilities betweenthe nodesand their immediatecauses fromthe experts. This stage reflects encodingof
the prior knowledgeabout the design problem.Thecausal modelmaybe used as a mechanism
for negotiation betweenseveral experts. Eachexpert mayworkon a separate part of a global modeland, then, negotiate languageandinteractions amongdecisions with the other domainexperts. Typically, a numberof
design decisions will have impacton morethan one pan of the global model.It is up to the experts to
identify those decisionsthat affect their viewsof the designproductandreconciletheir viewswith others.
¯ Definition of a collection ofconnectionist networksfor each cluster of design decisions. Eachnetwork
addressesa designdecision madewithin the enclosingdesignperspective(i.e., a cluster of designdecisions madeby a designagent). Theseconnectionistnetworksare initialized in sucha wayso as to reflect
the initial knowledgeencodedin the causal network.Theywill "wrap"aroundthe decision clusters and
becomeadvisors for the particular design agent concernedwith this set of designdecisions.
¯ Refiningof the initialdesign knowledge.The original transition probabilities given in the causal network
modelare refined by performingcormectionist learning based on existing design cases. As the design
243
knowledge
accumulatesin the formof the designcases, the initi’,d knowledgestructure will be further
refined. Theconnectionistdesignadvisors will observethe portions of the designdescriptionswhichare
of concernfor their respective decision clusters and updatetheir weightstates and, consequently,probability distributions of the causalgraphstructure.
Synthesizingnewdesigns by using advice fromthe design advisors. For a given set of designspecifications, constraints, anddesired performances,
the updateddesignadvisorevaluatesalternative decisionsin
the designprocessand prt~actively advises the designagent about the utility of alternative decisions.
Restructuringand refining design advisor knowledge.The causal networkstructure is restructured upon
discoveryof newcausal relationships. In the cases whenthe newdesign knowledgecan’t be encodedin
the existing causal structure and connectionist networks,updatingof these structures is necessary. It
seemsthat the notion of causality [Spirtes 931 maybe an importantconcepton whichIo base tile restructuring process. This is a conjectureat this point of time andit will be morethoroughlyinvestigated.
4 Issues
Thereare manyissues that needto be addressedin this research. Theprincipal issues are:
*
Designspace representations for machinelearning. There are manyexamplesof complexrelationships
that are importantat somelevel of reasoningaboutan artifact. Abstractionsof these relationshipsin order
to reasonabout themin the early designstage, and their com~ectionistrepresentations, require considerable thought and ingenuity.
¯
Integration and refinementof existing domainknowledge.In manycases, a priori qualitative knowledge
about designrelationships exists andit is beneficial to encodethis knowledge
in a leamingsystem. Machinelearningoughtto refine the initial knowledge
state, rather than start fromthe tabula rasa state.
¯
Verification and explanation of the knowledgeacquired through machinelearning. A comprehensive
wayof verifyingof the knowledge
acquiredthroughlearningis necessaryor, at least, an explanationfacility for the reasoningprocess oughtto be provided.
¯
Incrementallearning and design space extension. Facilities for incrementallearuing and design space
extensionare necessaryto support "true" learning capabilities for design: as newdata are available,
changesneed to be madeto the underlying knowledgestructure.
¯
Complexityof designadvisors. In realistic designproblemsit is necessaryto establish whatis the complexity of interactions amongthe designadvisors and the complexityof designadvisors within a design
cluster. In the case of the problemsof high complexity,it maybe necessaryto introducesomesimplifying
assumptionsabout the importanceof someof the advisor interactions.
5
References
[Finger92] Finger, S., M.S. Fox,F. B. Prinz, andJ. R. Rinderie, Concurrent
Design.AppliedArtificial Intelligence, voi. 6, no. 3, pp. 257-83,July - Sept 1992.
[Ivezic 92] Ivezic N., NETSYN:
A Connectionist Approachfor Acquiring and Using EngineeringSynthesis
Knowledge.Masters Thesis, Departmentof Civil Engineering, CamegieMellonUniversity, 1992.
[Pearl 88] Pearl J., Probabilistic Reasoningin Intelligent Systems:Networksof Plausible Inference. Morgan
Kaufmann
Publishers, San Mateo, Califoruia, 1988.
lPohl 92] Pohl J., J. La Porta, K. J. Pohl, and J. Snyder,AEDOT
Prototype(1.1): An Implementationof the
ICADSModel. Design Institute Report CADRU-07-92,
CADResearch Center, College of Architecture
and EnvironmentalDesign, Cal Poly, San Luis Obispo, 1992.
[Spirtes 93] Spirtes P., C. Glymour,and R. Scheines,Causation,Prediction,andSearch.SpringerVerlag,’93.
244