Cognitive Processes in Architectural Design

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conducted for p ro tocol ana lys i s . Based on the pro tocolt ranscr ip t ions , p roduc t ion sys tems a re mad e to s imula tethe c on t r o l s tr u c tu r e . T hus , t h e de ve lope d f ra me w or k isactual ly a cogni t ive mo del which i s capab leo f m a p p i n gthe wh ole des ign process . Resu l ts ob ta ined a re exp ec tedto ver i fy the m od el and to i l lus t ra te the cog ni t ive ac tiv i-t ies and t h i n k i l ~ phe nome n on oc c u r r i ng w i th in t he

des ign process .

A R C A -H T E CT U R AL D E S I G N P R O B L E MS O LV I N G

A c c or d in gto N e w e r a n d S i m o n 11,

a person (is) confronted with a problem when h e wantssomething and does not know immediately what series ofactions he can perform to get it,

Usua l ly, a p roblem conta ins the in i ti a l s itua t ion o f theprob lem so lver and i s re fe r red to as the in i t ia l s ta te . Agoal s ta te i s the s tage a t which the problem has beenreso lved . The process of p roblem so lv ing f rom in i t ia ls ta te to the goa l s ta te can b e mo deUed as a se r ies oft ransformat ions genera t ing a seque nce of p roblem s ta tes .A problem s ta te is a par t icu la r s tage in wh ich a prob lemsolver know s a se t o f th ings , and i s re fe r red to as aknowledge s ta te . The var ious s ta tes tha t the problemsolver can ach ieve a re ca lled problem spaces . The var iousw a ys o f c ha n g ing one s t at e i n to a no the r a r e op e r a to rs* .

In a rch i tec tura l des ign , the problem space inc ludes thefo l lowing components .

A se t o f des ign uni t s wh ich i s e i ther g iven in i ti a lly byc l ien ts as the d es ign prog ramm e or br ie f , o r i s gener-a t e d by t he de s igne r a t a ny i n t e r me d ia t e p r ob l e msta te . The des ign uni t s a re a l l phys ica l dements ofbu i l d ing c omp on e n t s t ha t a r e c o n s id e re d o r ma n ipu -la ted dur ing problem so lv ing . For example , a des ignun i t ma y be a l i v in g r oom, a d in ing r oom o r aba thro om in a res ident ia l des ign .

A se t o f opera tors which is no t spec i fied by c l ien ts bu ti s a par t o f the des igner ' s knowledg e base . Th e

opera tor i s anyth ing tha t changes the kn owledg e sta te.I t can be a r i thm et ic ru les for num er ica l ca lcu la tion , o ra se t o f ru les for a l loca t ing or genera t ing a des ign uni t .

A se t o f des ign cons t ra in t s th a t i s spec i f ied by c l ien tso r ge ne r a t e d by t h e de s igne r. Fo r e xa mp le , a de s igncons t ra in t can b e th e l imi ta t ion of to ta l f loor a rea.

A goa l in which the des igner finds an ob jec t sa t is fy ingal l of the constraints .

Based on the def in i t ion , a p roblem space can be formu-lated as:

Pro ble m space = {{goal} , {design un it} , {operato r} ,{constraint}}

Th e k now ledge s tate in des ign problem so lv ing is a s tage

*These definitions are taken fro mAn derson 16,and N ewell andSimon11.

i n w h ic h t he de s igne r know s t he de s ign un i t , d e s igncons t ra in t and appl ied ru les . Th en a s tate can be mov edforward by apply ing the ru les which sa t i s fy the se t o fconstraints .

A C O G N I T IV E M O D E L

W he n a de s igne r w or ks on a p r ob l e m spa c e a nd s e a r c hesfor solut ions, the involved cognit ive act ivi t ies can bemod el led as fo l lows .

A de s ign t a sk c a n be b r oke n dow n in to a s e que n c e o fgoa ls . T he genera t ion of goa ls der ives e i ther f rom a goa lp lan tha t i s s to red in me m ory or f rom a perceptua l - tes t.Th e m eans o f se lec ting a goal to work on i s re fe r red to asthe co nt ro l s tra tegy. The goa l p lan conta ins a sequence ofgoa ls tha t the des igner mus t k now in order to process thedes ign task , and mu st ach ieve in order to ge t the des ignprob lem in to the f ina l goal s ta te . In accompl i sh ing a goa l ,

the des ign er manipu la tes a se t o f des ign uni t s . A packag eo f know le dge a bou t t he de s ign un i t c a ll ed a s c he ma ,wh ich conta ins assoc iated des ign cons t ra in t s and ru les forappl ica t ion , i s s to red in a kn owledg e base as a par t o f thedes igner ' s long- te rm me mo ry. By tak ing a se t o f des igntwi t s an d re t r iev ing i t s associa ted schema ta , des ign so lu-t ions for a par t icu la r goa l a re genera ted and tes ted . Thisprocess can be i l lus t ra ted wi th in a s impl i f ied d iagram inFigure 1 . By repea t ing the process ( tak ing a goa l ,ac t iva t ing a des ign uni t , re t r iev ing a se t o f assoc ia tedschemata , apply ing a ru le to search for a so lu t ion andthen tes t ing the so lu t ion) , the des ign problem gradua l ly

mov es toward th e f ina l goa l .T h i s mod e l c on t ain s s e ve r al ke y c ompone n t s : k n ow -

ledge base , cont ro l s t ra tegy, des ign cons t ra in t s andsearch. The fol lowing sect ions wil l br ief ly discuss each.

A r c h i t e c t u r al k n o w l e d g eb a s e

Knowledge has been ca tegor ized as dec la ra t ive know-

_ l C u r r e n t ~ G o a l p la n I

Memory/

_ ~[Knowledgek l b a o I

F/gate I. A geneva/mode/of the des/ga p'ocess

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ledge , which com pr ises the fac ts we know , and proc edu-ra l knowledge tha t com pr ises the knowledge o f how toper form 16-1s . Dec la ra t ive knowledg e i s conce rned wi thknowledge as s ta t ic information. I ts representat ion hasbeen propo sed by semant ic ne two rk theory 19'2. P ro-cedura l knowledge i s the knowledge o f knowing ho w toper form a task . In per fo rming a task , the dec la ra t ive

in format ion i s t ransformed in to a p rocedura l fo rm. Theprocedura l knowledge i s commonly represen ted by p ro-duc t ion sys tems H , an ex tens ive ly deve loped and a w ide lyaccep ted represen ta t ion o f hum an cogni tive sk il ls .

Sc he m a th e o ry

R u me lh a r t a n d O r to n y21 have argued that semanticne twork theory i s no t ab le to account fo r the ab i l i ty toorgan ize , summar ize and re t r ieve in format ion aboutconnec ted sequences o f even ts . The y sugges t tha t schem atheory, which s tems f rom semant ic ne twork theory,m ore appropr ia te ly represen ts the kn owledge in genera l.Th e concep t o f schema is tha t a l l knowledg e i s g roup edin to un i t s and these un i t s a re schemata . Embedded inthese g roups o f knowledge i s , in add i tion to the kn ow-ledge i tse l f, in format ion abou t how th is kno wledge i s tob e u s e dz~'22. He nce , a sche ma h olds bo th declarat iveknowledge and procedura l knowledge . O ther charac te r-is t ics of schemata include:

schemata have var iab les and assoc ia ted knowledge

about the variables and their interrelat ionships schemata can be embe dde d , one wi th in ano ther ; a

schema is a ne twork o f subschemata schem ata represe nt know ledge at a l l levels of abstrac-

tion s c h e m a t a r e p r e s e n t k n o w l e d g e r a t h e r t h an

defin itions 21

These charac te r i st ics exp la in tha t the schem a can be seenas a da ta s t ruc tu re represen t ing the gener ic concep tss to r ed in me m o ry.

In a rch i tec tu ra l des ign , know ledge can be represen tedby a hierarchical sem antic n etw ork 14'15. Since a design erm ust hand le des ign un it s dur ing the p rocess o f des ign ,des ign un i t s a re sub jec ts o f the p rocess ing o f des igninformat ion . There fore , i t i s appropr ia te to represen tnodes in the semant ic ne twork by des ign un i t s . Des ignuni ts in the ne twork a re g rouped accord ing to re la tedarchitectural functional re lat ionships. A designer musthave know ledge o f the genera l compon ents (des ign un it s )o f a bu i ld ing as wel l as generic know ledge o f wha t theyare and how to des ign them. There fore , by the app l ica -t ion o f schema theo ry, i t i s assume d tha t a se t o f schematawhich con ta ins a la rge amount o f in format ion (des ign

knowledge) i s assoc ia ted wi th each des ign un i t in thesemant ic ne twork . A schema in the ne t cons is t s o fvariables (design unit) , the value of the variable (at tr i-bu tes ) and knowledge ab out how to use it . A l l the p iecesof knowledge assoc ia ted wi th des ign un i t s a re h ie ra rch i-

cally organized and the whole s tructure is cal led theknowledge base .

De s ign cons t ra in ts

As d escr ibed p rev ious ly, a r ich se t o f schemata assoc ia tedwi th a des ign un i t i s embedded in the knowledge base .Am ong them is a se t o f schemata ca l led des ign cons t ra in tschemata which a re assumed to be the mos t impor tan tones used in the course o f des ign . Before d iscuss ing theimpor tance o f schemata to the des ign task , the def in it ionof design constraint a nd related notions are explained.

Def in i t ion

The des ign cons t ra in t i s ana logous to the p rob lem con-s t ra in ts defm ed b y Re i tman as ' a t t r ibu tes o f ob jec ts 23. InS imon ' s def in i t ion , cons t ra in ts a re as ' bounds on thema gnitude of certain variables l . In this research, thedes ign cons t ra in t i s de f ined as ce r ta in requ i remen ts tha tmus t be fu l f i l led in o rder to des ign a des ign un i t o r agrou p o f design units . This defini t ion is s imilar to thatused by Eas tm an 13, o r des ign param ete rs used byAkin9,14.

Design cons t ra in t schemata

A des ign cons t ra in t schema which i s bound to a des ignuni t con ta ins the fo l lowing components .

An identifier. Theidentif ier is the name tag ofth econstraint .

A variable.A schema ma y have one o r more var iab les .The var iab le i s a des ign un i t. I f the re a re two or morevariables, then a cross-reference among the variablesis developed.

A set o f rules. Th es eru les app ly to knowing how tosa t is fy the cons t ra in t o r how to f red ou t the va lue o fthe cons t ra in t . Des ign know ledge i s em bed ded in th ispar t and can be represen ted by a se t o f p roduc t ionsys tems .

A value of the variable.The va lue resu l t s f rom theappl ica tion o f ru les and i s boun d to the des ign un i t.Th e va lue o f the var iab le may be a numb er, a li s ts tructure representing facts , or the topological andgeometr ic magn i tude o f the des ign un i t .

Whenever a des ign cons t ra in t schema is evoked , a copyof the schema is mad e in o rde r to b ind a par t icu la rconfiguration o f values to a part icular c onfiguration o fvariables at a part icular moment in t ime. This is thenotion of an instantia tion of a schem a 22. Th e value of aschema is genera ted by the eva lua tion o f the assoc ia ted

set of rules in order to f i t the exist ing si tuation, and thisva lue is then bou nd b ack to the var iab le . Such a schema(which has an updated value) is cal led an instantia tedschema. N ew know ledge is the re fore accumula ted by theinstantia t ion of schem ata.

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Categories o f constraints in d esign processes

Design cons t ra in ts can b e c lassi f ied in to two k inds . Th eftrs t kind includes global constraints applicable to agroup o f des ign un i ts . Th ey m ay be g iven by c l ien ts o rgenera ted by the des igner a t an ea r ly des ign s tage , andare ca r ried th rough the whole des ign p rocess . The second

kind is local constraints or what Simon has calledau tonom ous cons t ra in ts 1. The au tonom ous cons t ra in tsa re ne i ther impl ied by the in i t ia l p rob lem requ i rementsnor p rov ided by the c l ien t . They a re bound to a un iquedes ign un i t and a re a r ich se t o f des ign reper to i re s to redin the des igner 's long- te rm m em ory. Only a smal l subse to f au tonom ous cons t ra in ts ma y com e in to ac t ive use inthe cou rse o f des ign ing any s ing le ob jec t. T hey wi ll beevoke d by par t icu la r s i tua t ions tha t a r i se in the course o fthe des ign , re t r ieved f rom memory, and then app l ied .

On e o f the charac te r is t ics o f an i l l -def ined p rob lem isi t s huge p rob lem space . In o rder to save the search ing

effor t in the la rge p rob lem space , a des igner wouldin t roduce a des ign cons t ra in t to reduce the p rob lemspace fo r so lu t ion genera t ion . In o rder to f red an op t imalso lu t ion , the des igner would a lso in t roduce o ther con-s t ra in ts , w hich have be en descr ibed as c r ite r ia by Akinetal. 9, to te s t the genera ted so lu t ion . For these reasons ,design constraints are crucial in design problem solving.

Co ntrol strategy

W h e n th e p ro b le m s p a c e b e c o me s mo re c o mp le x , p ro b -lem so lvers a re more l ike ly to have a p lan deve lopedb e fo re h a n d . T h e p l a n h a s b e e n d e f in e d b y M i l le ret al.asa h ie ra rch ica l p rocess tha t con t ro ls the o rde r in wh ich asequence o f opera t ions i s to be per fo rm ed 24.

Go a l p l a n

Descr ibed in the model , goa ls a re deve loped f rom twosources . One i s f rom a goa l p lan , the o ther i s f romperceptual- test . In architectural design, designers have agenera l des ign me thod , as descr ibed by He a th 2s, s to redin the i r long- te rm memory ca l led a genera l goa l p lan .This goa l p lan cons is ts o f a seq uence o f genera l goa lswhich the des igner mus t accompl ish tha t w i l l gu ide thedes ign p rocess . Dur ing the p rocess , the des igner mayperce ive a po ten t ia l p rob lem tha t he o r she m us t so lve a ta par t icu la r knowledge s ta te ; a subgoa l i s fo rm ed accord-ing ly. Th e opera t iona l re la t ionsh ips be tw een a subgoa land the goal that a designer has reached is betterexp la ined by the concep t o f goa l s tack .

Go al s tack

T h e s h o r t- t e rm me m o ry i s a s s u me d to h a v e th e fo rm o f as tack which con ta insgo a l s 26.The goa l p lan in long- te rmme mo ry ho lds a l is t o f symb ols represen t ing goa ls . Thefirs t symbol at the goal plan is act ivated and therefore

he ld in shor t - te rm memory as the cur ren t work ing goa l .I f a goa l cannot be accom pl ished , a new subgoa l w i l l bed e v e lo p e d a n d a c t iv a t e d o n s h o r t- t e rm me mo ry a n d th eprev ious goa l i s pushe d ba ck to the goa l s tack and s to red .

Perceptual-test

The des ign so lu tion accumula tes f rom s ta te to s ta te , andinformation presented in external display is changingaccord ing ly. A des igner mus t ga ther in format ion aboutthe p rob lem s i tua t ion f rom t ime to t ime and th is i s doneby percep t ion . Research s tud ies on percep t ion in p rob-lem so lving have dea l t w i th the percep t ion o f chesspos i t ions 27 '2s , o rso lv ing the Tower o fH a n o i p u z z l e 2 6.T h e p e rc e p t io n h a s b e e n fo rmu la t e d b y p ro d u c t io nsys tems to descr ibe the func t ion o f i ts mechan ism , and i sreferred to as p ercep tual- test 26.

Th e condi t ion o f percep tua l - tes t invo lves a se r ies o f

tes ts , whi le the ac t ion invo lves a sequence o f e lementa ryactions. The test in the condit ion part is usually a test ofthe p resence o r absence o f a par ticu la r k ind o f sym bol inthe goa l s tack to de te rmine the appropr ia te s tep . Theac t ion may be a motor ac t o f d rawing , da ta inpu t ,solution generation or solution test ing. The result wil lgenera te new in format ion and change the con ten ts o fs h o r t- t e rm me m o ry.

Th e concep t tha t the percep tua l - tes t se rves as a po in te rconnec t ing to the nodes in the knowledge base has beenp ro p o s e d b y L a rk inet a L 29 .Whenever a des ign un i t i sp resen ted , i t i s pe rce ived by the sys tem. Then theperceptual- test serves as an index to access the informa-t ion s to red in the knowledge base . O ther mechan isms ofpercep tua l - tes t a re assum ed to be ab le to:

te s t whe ther the cur ren t goa l has been ach ieved test wh ethe r the generated solution sat isfies global

constraints perceive which design unit is lacking at the curre nt

design stage p e r c e iv e t h e p ro b le m c o n te x t t o d e t e rmin e th e

appropriate s tep to follow

By us ing these mech an isms , pe rcep tua l - tes t p resum ablyserves the following functions.

Th e test of goal s ta te wil l guarantee th at the system isa lways in p rogress and tha t the p rocess a lways movestoward a goa l. Th us , i f the cur ren t goa l has beenach ieved , then the sys tem wi l l re t r ieve the nex t goa lf rom the goa l p lan o r the goa l s tack . O therwise , thepercep tua l - tes t w i ll pe rce ive which i s the nex t cand i -da te des ign un i t in o rder to con t inue accompl ish ingthe cur ren t goa l.

The tes t o f g loba l cons t ra in ts w i l l mak e sure tha t thegenerated solution is optimal. If the generated solu-t ion sat isf ies a l l the constraints , then the system willp roceed to the nex t des ign un i t under the cur ren t goa l .O therwise , a new goa l i s se t up .

I f a des ign un i t i s p resen ted in shor t - te rm m em ory and

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a se t o f cons tra in t schem ata i s evoked , the percep tua l -test will recognize that such a design unit m ust b eso lved in o rder to p rocess the nex t one . Thus , asubgoa l i s deve loped to so lve the p rob lem be ingpresen ted .

The percep tua l - tes t w i l l pe rce ive what happe ns a t thecur ren t s ta te and wi l l de te rmine an appropr ia te s tep to

process .

When a goa l i s genera ted f rom a goa l p lan , i t i s goa l -d r iven . I f a goa l i s deve loped f rom p ercep tua l - tes t , i t i spercep tua l -d r iven o r s t imulus-dr iven . T he way of se lect -ing a goa l o r the wa y of s truc tu r ing a so lu t ion pa th i sreferred to as control s trategy. The control s trategy wil lp rov ide c lues to how a des igner s t ruc tu res the so lu t ionpa th .

cand ida te so lu t ions o r com ponen ts o f so lu t ions . T he tes twil l determine whether a candidate sat isf ies a set ofconstraints . B y using the test informa tion, the generatorproduces a new knowledge s ta te by modify ing a s ta teprod uced prev ious ly in the search . Th is depend ency ofgenera t ion upon the tes t ou tcome charac te r izes theheur ist ic sea rch method . Any ob jec t genera ted tha t

sat isf ies the test process is guaranteed to sat isfy al l thedesign requirements . In a generate-and-test process, thedes ign i s assembled component by component . Eachgenera ted component i s added to the p rev ious compo-nen t and a new assembly i s c rea ted and tes ted . I f the tes tsucce eds, the proc ess continues; i f i t fa ils , the ne wcom ponent i s d iscarded and ano ther one genera ted . Thedesign proce ss contains a series of generate-and-testcycles.

S e a r c h

Simon ind ica tes tha t p rob lem so lv ing ac t iv i ty can bedescr ibed as a sea rch th rough the p rob le m space , un t il as ta te i s reached tha t p rov ides the so lu t ion to theprob lem 26. Thus , the w hole p rocess i s a sea rch th roughthe knowledg e s ta tes gu ided by in format ion ac cumu la teddur ing the search . There a re many search methodsd iscussed in AI l i te ra tu re . Only th ree bas ic ca tegor iesc lass i f ied by Newel i and S imon a re used here , as thesemethods p rov ide p r imary and fundamenta l exp lana t ionsto the cogni t ive p rocesses n .

Recognition

The recogni t ion method i s defmed as knowing theanswer. I t happens when the p rob lem is reduced to ap o in t a t w h ic h a k n o w n p ro c e d u re o r mo d e l c a n b eappl ied to the remain ing s tages . The k now n proced ure o rmodel has been descr ibed by S imon as ' p re fabr ica tedso lu t ions ' , which p rov ide answers to subprob lems tha tarise repe atedly in different contex ts ~. I t h as also beendescr ibed as ' p resoh i t ion mo del ' by Foz 3. The re t rieva lof the p reso lu t ion model i s done by percep t ion , whichge ts access to the index of in format ion in the know ledgebase , and thus i s ca l led the recogni t ion method .

Means--end analysis

The means-end ana lys is requ i res a knowngoal, theiden t if ica tion o f d i ffe rences tha t ex is t be tw een the cu r-ren t s ta te and the goa l s ta te and the se lec t ion o f opera to rsthat wil l reduce these differences.

Generate-and-test

Th e genera te -and- tes t meth od inc ludes a genera to r and atest 2 . Th e gene rator wil l take de sign un its an d a set ofcor responding schemata to genera te ob jec ts tha t a re

A L A B O R AT O R Y E X P E R I M E N T A N D D ATA

C O L L E C T I O N

The cogni t ive model de l inea tes des ign p rob lem so lv ingprocesses in genera l. Fo r the purpo se o f jus ti fy ing themod el and of observ ing the cogni tive ac tiv it ies in des ign ,a labora to ry exper iment w as des igned nex t .

Ta s k a n d su b j e c t

The task was adap ted f rom the des ign p ro jec t docu-menta t ion used fo r Des ign Leve l 2 S tud io in Spr ing

1986, Depar tm ent o f Arch i tec tu re , Carneg ie Mel lonUniversi ty. The original design instruction had beens impl i fied to f it the exper imenta l purposes . T he task wasto des ign a th ree-bedro om dwel l ing fo r a s ing le fami ly ona la rge p roper ty in the nor thern campus . Des ign un i t sinc luded a l iv ing room, d in ing room and two bedroomsfor a son and a daughte r. The to ta l f loor a rea was l imi tedto 2 200 squ are fee t . Th e cl ient was a professionala rch i tec tu ra l pe rspec t ive d ra f t sman . Tw o image un i t s* , aD o r i c c o lu mn a n d a b a y w in d o w, w e re r e q u i r e d t o b eincluded in this residential design. The accurately scaledf loor p lan , e leva tion and sec t ion d rawing of the image

uni ts were a lso p rov ided . Th e purp ose o f hav ing imageuni ts was to observe when and how a des igner dea ls w i thimage par t .

A profess iona l workshop , which i s no t common in ares iden t ia l dwel l ing , was a lso requ i red to observe how ades igner p rocesses an unfami l ia r des ign un i t . T he des igninformat ion was reduced to a min imum to d iscern thek in d o f k no w le d g e th a t c a n b e r e tr ie v e d f ro m m e mo ry.The sub jec t was a PhD in Arch i tec tu re s tuden t enro l leda t Carneg ie M el lon Univers i ty. A t the t ime of the s tudy,he had e igh t years o f des ign exper ience and had work edfor a professional f irm for approximately two years .

*An ima ge unit is defined as a specific architectural form that isdeveloped by the client. A designer will perceive such a formand develop an image code in his or her long-term m emory inorder to pro cess the design task 31.

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P r o c e d u r e

T h i s e x p e r ime n t w a s c o n d u c te d a t t h e D e s ig n a n dIn fo rma t io n P ro c e s s in g L a b o ra to ry i n t h e D e p a r tme n t o fArch i tec tu re . I t began r igh t a f te r the sub jec t f in ishedread ing the task ins t ruc t ion . Drawing paper and markerp e n s w e re p ro v id e d . T h e s u b je c t c o u ld d r a w a n y th in g h e

wished, but a f inal s i te plan, f loor plan and facadedrawing we re requ i red to be f in ished a t the end o f theexper imen t . Th ere was no time l imi ta t ion . T he sub jec twas asked to speak a loud a t a l l t imes whi le he worked ,and h is verba l iza t ions and drawings were recorded onv ideo- tape . The exper iment las ted fo r about four hours(232 minutes) .

M e t h o d o f d a ta a n a l y s is

M ethods o f da ta ana lys is requ i red f ive sequen t ia l s teps.

Transferring data into protocol transcription

The raw da ta was t ransfe r red in to a p ro toco l t ranscr ip -t ion (a l is t in writ ten form of the subjec t ' s verbalizat ions) ,re fe r red to as s ta tements in the exper iment . S ta tementswere segmented by any pause g rea te r than 4 seconds .This method i s d i ffe ren t f rom the 2 second pause t imeused by Byrn e 3z because th is ex per iment invo lved v isua lpercep t ion o f d rawings . S imon ind ica tes tha t a fewhund red mi l l iseconds to a couple o f seconds a re needed

to r e t r i e v e i n fo rma t io n f ro m me mo ry33. Posner statestha t m em ory can ho ld v isua l ly perce ived in format ion fo r2 seconds 34. Tak ing the up per boun ds , the reac t ion t imeof ho ld ing a v isua l ly perce ived i tem in shor t - te rm mem -ory p lus re t r iev ing in format ion f rom memory i s es t i -mated to be 4 seconds . A pause t ime grea te r than 4seconds ind ica tes tha t the success ive s ta tement p roba b lyp ro v id e s i n fo rma t io n a b o u t a n e w p e rc e iv e d i t e m.Accord ing to th is method , th is p ro toco l con ta ins 604s ta tements .

Identifying episodes

After the p ro toco l t ranscr ip t ion had been comple ted , i twas c lass i f ied in to ep isodes . An ep isode i s de fmed byN ew er and S imon as ' a succ inc t ly descr ibab le segment o fbeh aviou r associated with at ta ining a goal 11. Eac h epi-sode con ta ined a un ique goa l tha t was to be ach ieved , a ndwas t rea ted as one un i t ep isode . The goa l in an ep isodewas identif ied:

by the verba l in format ion in p ro toco l t ranscr ip t ion by t rac ing a ser ies o f ac t ions which a t tem pted to so lve

one des ign un i t by a par t icu la r recognizab le in ten t ion under w hich a

group of des ign un i t was to be reso lved

The purposes o f deve lop ing ep isodes and iden t i fy ing

goa ls were to observe the mechan ism tha t de te rminedgoa ls , and to fmd ou t how goa ls were in i t ia ted andte rmina ted . The re w ere 22 ep isodes in th is exper iment .

Identifying know ledge states

After ep isodes had been iden t i f ied , knowledge s ta tes inepisodes w ere clarif ied. Specif ical ly, a know ledge state isa s tage o f know ledge in w hich som e p ieces o f in format ionare ac t iva ted in shor t - te rm memory. Any change ofknowledge s ta te symbol izes a move , and a lso marks anappl ica tion o f an opera to r. Th us , the t race o f a mo ve ofknowledge s ta te i s based on any changing in format ionoccurr ing in the s ta tements . In th is s tudy, the purposeswere to unders tand w hat k ind o f knowledge appears in aknowledge s ta te , under which des ign un i t i t was cons i -dered , and wh at so r t o f opera to rs caused the move .

Problem behaviour graph development

Th e kn owledge s ta te and i t s mo ve on ly p rov ide fragmen-ta l in format ion . In o rder to unders tand the whole se -quen t ia l move s in ach iev ing a goa l , a p rob lem behav iourgraph n i s used . T he p rob lem behav iour g raph i s aconc ise express ion o f mov es o f knowledge s ta tes. N ode srepresen t knowledge s ta tes and l ines symbol ize t rans-fo rmat ions in the g raph .

The par t ia l p rob le m beha v iour g raph of the sub jec t i sshown in F igu re 6 . I t i s coded accord ing to the taxonomy

given in Tab le 12. Th is p ro b lem b ehav iour g raph shouldbe read f rom le f t to f igh t , then down. The des ign un i ttha t i s be ing cons idered i s shown on to p o f the l ine . T heopera t ions tha t were used fo r s ta te t ransformat ion a reshown be low the l ine . The ques t ion ma rk represen ts da tainpu t f rom the exper imente r. GC represen ts g iven con-s t ra in ts . RC m eans re t r iev ing cons t ra in ts f rom m emo ry.NC means newly genera ted cons t ra in ts . RCXX/G s tandsfor generating a solution by applying the rules that areassoc ia ted to the cons t ra in t XX. RCXX/T s tands fo rapp ly ing the re t r ieved cons t ra in t XX to tes t the resu l t .The far lef t vert ical l ine symbolizes goal s tack.

T h e p u rp o s e s o f c o n st ruc t in g th e p ro b le m b e h a v io u rgraph were :

to observe how a goa l was ach ieved to unders tan d the pa t te rn o f move s t o d e te c t h o w s e a rc h me th o d s w e re imp le me n te d

T h e b a c k -u p o f a k n o w le d g e s ta t e d o e s n o t m e a n th a t t h eknowledge has been abandoned . Ra ther, i t s ign i f ies thechange o f knowlege s ta te tha t cor resp onds to e i ther goa ldeve lopment o r sea rch ing e ffo r t .

Discovering the invariant structure

Fro m observ ing the typ ica l p rocess ing pa t te rn ex h ib i tedin the p rob lem behav iour g raph , and f rom f i t t ing da ta

Vol 11 N o 2 April 1990 65


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in to the cogni t ive model to observe the cons is tency ofprocess , the invar ian t cogni t ive s t ruc tu re which repre -sen ts the sys tem' s behav iou r was ab le to be de tec ted .

Reliab ility of data collection

In test ing the rel iabil i ty of data collect ion, a port ion ofthe p ro toco l t ranscr ip t ion ( the p rocess o f so lv ing Do r icco lumn and ba y window) , was g iven to a th i rd person , anarch i tec t, fo r the purpo se o f cod ing the p rob lem be-hav iour g raph . He was p rov ided w i th a se t o f spec if iedprocedures to be fo l lowed . A l though he d id no t have anins igh t unders tand ing of the sub jec t ma t te r in th isresearch , the pa t te rns d isp layed in h is resu l t s showmany similari t ies to the experimenter ' s . In part icular, thefour genera te -and- tes t sea rch cyc les on so lv ing the Dor icc o lu mn a n d b a y w in d o w w e re d e t e c t e d b y b o th g r a p h s(see F igure 6 ) . Th is shows tha t the meth od deve loped fo r

observ ing the change o f knowledge s ta te to exp lore thesearch effort is pert inent . Also, his identif ied operatorstoge ther w i th the d iscerned knowledge s ta tes were inagreem ent w i th o ur resu l t s unde r the app l ica tion o f thesame me thod . Th is ind ica tes tha t , b y fo llowing the samem ethod , the d isc r imina t ion o f knowledge s ta tes andopera to rs tha t cause the move can be made exp l ic i t ly.There fore , the method be ing deve loped prov ides anexac t norm for ga ther ing da ta a bout des ign p rocesses .

R E S U L T S O F T H E P R O TO C O L A N A LY S I S

Resul t s ob ta ined f rom co l lec t ing and ana lys ing da taaccord ing to the me thods de scr ibed a re exp lained in the

following sections.

Know ledge base

The des ign un i t s which were re t r ieved and used by thesub jec t in th is exper im ent a re show n in te rms o f a t ree

s t ruc tu re (F igure 2 ) . Th is i s the p roposed knowledgerepresen ta tion o f the sub jec t in th is des ign . N odes a reeither design un its or a c lass of design units hea ded b y anabstract name. Arranging this tree s tructure requiredt rac ing the o rder o f appearance o f des ign un i ts in thepro toco l and then g roup ing the des ign un i ts toge ther bycategories . Cross-references among nodes are not shownin this f igure.

This knowledge base p rov ides the des igner w i th anorgan ized ne tw ork o f in format ion tha t i s app licab le todesign. I t a lso provides a c lue as to which design unitshou ld be p rocessed nex t . Because o f th is o rgan ized

representat ion, an eff ic ient search is possible . However,this diagram should no t be construed as a l iteral mo del ofthe internal data s tructure being accessed, a l though i tma y se rve to sugges t som e proper t ies o f these s t ruc tu res.For example , the upper nodes on ly appear a t the ea r lydesign stage. Wh en design procee ds into la ter s tages,lower nodes g radua l ly appear in m ore de ta i led d rawings.Such an in format ion re t rieva l reveals a top-down process .

I t is assum ed that there is a prototypical representat ionfor a bu i ld ing type s to red in the long- te rm memory.W hen a des ign task is assigned , a des igner mu s t deve lopa new represen ta t ion to fi t the p rob lem a t hand . Such a

deve lopment i s s tepwise and gradua l . The whole repre -sentat ion wil l be rob ust and concrete af ter the designer isfami lia r w i th the na tu re o f the p rob lem. For ex ample , the

Housedesign

S i t e / ~ ~ ~ House

Building G ara ge L andsca pe Swimming Two-storey Doric Bay windowmass / k ~ pool building column

ShapeDriveway Car W alkway Tre e Garden Floor plan Elevation

~ " " ~ S e c o n d f o r ~ ~ RoofFirst floor~ Connection

Living Dining Kitchen Ba th roo m W ork sh op Staircase

I I I Ifireplace porch sink bath windowwindow window range sink doordoor door window WC occupantsentr anc e occu pants door windowI deck doorsteps backyard

MB R / " " " ~ B R C h i ! n e yI I W atertank

double bed single bedwindow study areadoor wardrobebathroom doorI windowdressing bathroom

I Icloset dressing

Figure 2. The representationof knowledge base

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after the combined image unit was generated andtested, the subject retrieved a new constraint whichwas that the structural dements must dictate howthings correspond inside. I t seemed to the subjectthat what he had been working on was half fromthe inside (function) and half from the outside(form). Although the goal, which was to solve the

image units, had been achieved, the generatedsolution did not satisfy the new constraint. So, heswitched the problem space to the next goal, whichwas to solve the functional layout.

o Cr/t/ca/prob/on s/mat/on. This happens at the selec-tion of solutions. For instance, in this experimentthere were four cycles of generate-and-test in solv-ing the Doric column and bay window. In eachcycle, one solution had been generated by oneconstraint and tested by another constraint. For thes e c o n d alternative solution, w h i c h w a s to u s e aDoric column as an interior single deme nt support-

lug the ceiling, the subject indicated two things: (1)such a form must match a classical vault, whichwould change the character of the house; (2) he wasn o t keen on doing a historical revival. Therefore,this solution was abandoned.

A l t h o u g h t h e s e c o n d reason suggests a p e r s o n a lpreference, the first one indicates that the subjectperceived a critical problem situation at the timewhen a solution was generated. The critical prob-lem situation refers to the possibility of changingthe problem structure* or solution path. In otherwords, the critical problem situation is the state of

affairs or position that will lead to a possiblerestructuring of the problem. In this example, thesubject perceived that the solution would cause thechan me of the interior fo rm, material, structure andthe character of space, Such changes may possiblyhave led him to restructive knowledge representa-tion or to change the goal plan, since historicalelements were involved. Therefore, the selection ofsolution was made upon the perception of theproblem situation and to avoid having a majorchange of the problem structure.

The third function is to perceive what is lacking at thepresent stage. The system searches for a design unit towor k on next. Table 3 gives an example cited from theprotocol statement. In this example, after the subjecthad solved the Doric column and bay window, hesearched for a new design unit to work on. Glazingwas the evoked new design unit in this instance.

The fo urth function is to perceive the problem contextand solution context to determine the next step or t henext action.o Problem context. The perception of the problem

context is to perceive the problem structure anddetermine the goal sequence. Two examples of thiswere found (see Table 4). The fas t example shows

*The problem structure means the format of knowledge repre-sentation, gnal plan and constraint establishment, and is theresult of problem structu~_g.

Tab l e 3 . Pe rcep t u a l - t e s t s ea rch es fo r n ew d es i g n u n i t s

#72:

#73

#74

I am trying to see, in terms of the plan, like here[previous floor plan drawing] I am trying to see in termso f s e ct i o n w h a t i s g o i n g t o h a v e , a n d I a m t r y i n g t o se ewhat other t h i n g scould be attached to this co|utah as anelement. ( 2 8 : 5 0 )

One t h i n g i s t h a t ,you may call it some kind of glazing,[draw a horizontal line across the column in the floorplan] in which the column. (29:15)

Really is a free-standing element, visually. (29:30)

O

that the subject perceived the size of the buildingmass as a small one, so he decided to do sitedevelopment later. It turned out that the sitedevelopment appeared at a later stage in the pro-tocol. The second example shows that the subjectintended to determine the scale of drawing byperception.Solution context. T h e perception of solution contextfunction is to perceive the solution path and deter-mine the next solution generation. For example,the choice of a symmetry constraint was developedoriginally from the development of the Doric col-umn and the bay window. The development of theDoric column and bay window had gone throughfour generate-and-test circles to satisfy several con-straints. The subject was satisfied with the finalresult of the centralized column which supportedthe bay window, so a symmetric centraliTationaesthetic principle was created accordingly. Fol-lowing the same aesthetic principle, the symmetryconstraint was again selected to solve the livingroom layout. As the subject indicated

Since i t [Doric column] is going to be somethingstriking as an element like that [Doric column], atleast here I am trying to keep this [living room] spacesand try to maintain the symmetric disposition.(113:40)

At this state, the subject perceived the solution

Ta b l e 4 . P e r c e p t u a l -t e s t d e t e r m i n e s g o a l s e q u e n ce s

# 3 3 : Thirty feet i s . . . what? It is a tiny house on a propertylike this! It is a very tiny house. (11:33)

#34: And it is going to be so tiny. (11:46)

# 3 5 : Then except for the major orientation, it doesn't matterwhere, one could place it later on on the site. Becausethere is so much of land around it. (11:56)

E ~ Z

#42: I am trying to get roughly that initial scale and gives mesome idea of scale. (15:21)

#43 So, i t i s appro~timately so much [draw a horizontal line].

(15:37)#44 I am trying to see whether I am going to work on this

scale, or I could work on just schematic on this scale,and then going into more details before I make any more . . spatial organization. (15:52)



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context and selected the next solution which f i t theso lu t ion con tex t . The so lu t ion con tex t means tha tthe occurrence of solution B is re lated to solutionA, o r that the resu lt of solution A leads to the causeof solution B. In this instance, th e result of solutionA (Dor ic co lumn mad bay window) resu l ted in theselection of symmetry for solving the l iving room

layout.

The d iscovered con t ro l s t ra tegy mad these func t ions o fpercep tua l - tes t conf i rm the p red ic t ion o f the mode l .Moreover, th is ev idence demons t ra tes tha t pe rcep tua l -tes t i s the p r imary mechan ism which de te rmines goa lsequences mad thus cons t ruc ts the so lu t ion pa th .

D e s i g n c o n s t r a i n t s

T h e p ro b le m b e h a v io u r g r a p h s h o w s th a t w h e n e v e r a

design solution is generated or tested, there is a t least onedesign constraint involved mad a rule is verbalized.Whenever a des igner works on a des ign un i t , a se t o fcons t ra in ts i s evoked f rom memory mad the assoc ia tedrules are applied.

Global constraint an d local constraint

Data show tha t the g loba l cons t ra in ts a re mos t l ike lydeve loped dur ing the fn ' s t ep isode (8 ou t o f 12), which i sthe task-unders tand ing s tage ( see F igure 6 ) . The n th ey

reappear a t lower level nodes . In th is exper iment , themo st d is t inc t g loba l cons t ra in t w as c l imate . T he c l imatefac to r in f luenced the space o rgan iza t ion (more abs t rac tlevel) , mad also affected the location of the windowopening mad the glazing size (more detai l level) . Theg loba l cons t ra in ts use d b y the sub jec t a re l is ted in Tab le5 . On the le f t o f the tab le a re cons t ra in ts g iven by thetask instruction, mad those on the r ight were retr ieved b ythe subject . These constraints ref lect the following char-acteristics:

they were mos t ly evoked a t the f i r s t ep isode

they were applicable to a group or to al l design units they were ab le to be used in d i ffe ren t des ign tasks

There are 47 local constraints in this protocol . For thework shop , the g loba l cons tra in ts were land s lope , c l imatemad natural l ight; whereas the local constraints were

location on basement, vis i tor accessibil i ty, noise andventi la t ion. A careful s tud y shows that global constraintsappeared a t bo th upper and lower leve l nodes , bu t loca lconstraints only appeared at the two lowest levels in theknowledge tree. This indicates that global constraintsgu ide the w hole des ign p rocess and a re used to genera teupper leve l des ign un i t s . When the upper des ign un i t s

have been so lved , the sys tem proceed s to the de ta il leve lmad local constraints are evoked.

Ru les in schemata

When a des ign un i t i s p resen ted on work ing memory, acons t ra in t schema is re t r ieved . Rules in schema a re thenused for solution generation or test ing. These rules ,which con ta in domain-spec i fic knowledge (des ign know-ledge) , can be represen ted by p roduc t ion sys tems . Forexample , the re a re th ree p ro toco l s ta tements in Tab le 6 .

These s ta tements assoc iate w i th the des ign un i t o f bu i ld -ing mass unde r the cons t ra in t o f s ite p r ivacy. Use d by thesub jec t to come u p w i th a so lu t ion , these ru les a re wr i t tenin p roduc t ion sys tems as shown in the same tab le .

In ru le 1 , the kn owledge o f fac t o r dec la ra tive know-ledge is knowing the p r iva te corner in the s i te i s em bed-ded a t the le f t-hand s ide o f the p roduc t ion . The p rocedu-ra l knowledge , wh ich i s to pu t the bu i ld ing a t the p r iva tecorne r, is a t the r ight-hmad side. The action side iscri tical in design pro blem solving, for i t contains dom ain-spec i f ic knowledge which i s heav i ly re l ied upon by thedes igner. The da ta ana lyses show tha t the re was a

t r e m e n d o u s a m o u n t o f d e s i g n -s p e c i f ic k n o w l e d g eem bed ded a t the ac t ion par t. In o rder to f ind the fac t inrule 1, the syste m will sequ entially instmatiate rule 2 mad3. Thus, rules in schemata are applied for solutiongeneration.

Da ta input

Data inpu t happens a t the t ime when a des igner needsmore in format ion about a des ign cons t ra in t . For ins t -mace , a f te r the semi-open workshop space had been

generated, the subject used noise constraint for a test . Inorder to fred out the level of noisiness, he rel ied on datainpu t . Th is shows tha t when a cons t ra in t schema isevoke d mad the va lue o f the cons t ra in t i s uncer ta in , o rthere i s no ru le to f red the va lue , then da ta inpu t i sneeded .

Ta b l e 5 . G l o b a l c o n s t r a i n t s f o u n d i n p r o t o c o l d a t a

Given Retrieved

Climate (LR , window, workshop, BR)(building m ass)(building m ass)(building mass)(building m ass)

(site)

Total floor areaLand slopeAccess roadSitearea

LightPrivacyNear access roadComm on bathroomBedroom w ith attached bathroomSymm etrical dispositionRoom dimension

(window opening)(LR , building mass)

(building mass)(bathrooms)(bedroom)

(floor plan, elevation)(rooms)

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Ta b l e 6 . P r o to c o l a n d r u l e s in s c h e m a

Protocolstatonen ts#18: Now, somehow, it seems that this [northeast] comer

here, seems m ore private. Because these two edges [westand south] are bou nd b y outside roads. (06:07)

#19: An d there is a property on this [north] side and a privateproperty on this [east] side. (06:21)

#20: So, things will be better, if I place things along this[northeastern comer] side. (06:28)

Rule representationRule 1: If there is a private come r

then put building at private com er

Ru le 2: If edg e A is privateand edge B is privateand A and B are adjacentthen the comer formed by A and B is a pr iva tecorner

Rule 3: I f edge is next to a prop ertythen this edge is a private edge

Operator

A n y t h i n g t h a t c a u s es t h e s t a te t r a n s f o r m a t i o n i s c a l le d a no p e r a t o r. T h e d i f f e r e n t k i n d s o f s ta t e t ra n s f o r m a t i o nf o u n d a r e c a te g o r i z ed i n F i g u r e 3 . R u l e s f o u n d f o rb ind ing va lue to va r iab le a re a r i thme t ic ru le s , a s se r t ionso r log ical indu c t ions . Ev id ence a l so shows tha t i f thes u b j e c t i s u n c e r t a i n a b o u t t h e r u l e , t h e n n o a c t i o n i se x e c u t e d .

Co nflict o f constraints

I f a gene ra ted so lu t ion i s in con f l i c t w i th the g loba lc o n s t r a i n t , t h e s y s t e m w i l l c h a n g e t h e p r o b l e m s p a c e .Th is i s a pa r t o f con t ro l s t r a tegy a s desc r ibed be fo re . I f agene ra te d so lu t ion i s in con f l i c t w i th loca l cons t ra in t s ,t h e s y s t e m i s s u p p o s e d t o m o d i f y t h e s o l u t i o n . H o w e v e r,in th i s expe r imen t the sub jec t t ended to sac r i f i ce theo r i g i n a l c o n s t r a i n t , a n d o n l y t h e n e w r e t r i e v e d c o n -s t r a i n t s w e r e t a k e n i n t o a c c o u n t . F o r e x a m p l e , t h ec e n t r a l i z e d k i t c h e n d o o r, w h i c h o p e n e d t o t h e d i n i n g

r o o m , w a s g e n e r a t e d b y o b s e rv i n g t h e i n t e rn a l s y m m e t r i cd i spos i t ion cons t ra in t . Bu t the re su l t was in con f l ic t w i th

Q Constraint. . Q Q Constraint ~Q

Data input or Use rules o bindinstantiated schema value to the variable

Operator: data input Operator: rule application

Q esign unit , , Q

Constraint schemataapplication

Operator: rule applicationMethods: Means-endanalysis

GenerateTest

Figure 3. Causes of state transformation

Q Designunit D @

Application ofpresolution model

t h e v i s u a l a c c e s s f r o m t h e l i v i n g r o o m , a n d w i t h t h el o c a ti o n o f t h e s w i n g o f t h e k i t c h e n d o o r. T h e s u b j e c tsa id ,

Uhm, two things, one is . . . because this kitchen door isjust in a wrong place. I am trying to get to smaller details,the reason being that, is absolutely central, l ike anyone whois sitting there [living room] is straight looking into thekitchen. A nother reason is that, w here is this swing of the[kitchen] door goes? I don't have a position for this. Youopen the door and that panel of the door that is going toswing them [user in the kitchen], which is not very sensible.(121:19)

T h e f i n a l s o l u t i o n , w h i c h w a s n o t c o n s i s t e n t w i t h s y m -m e t r i c d i s p o s i t io n , w a s t o m o v e t h e k i t c h e n d o o rt w o fee tt o t h e r i g h t . T h i s c a n b e i n t e r p r e t e d a s m e a n i n g t h a t t h ec o n s t r a i n t m a y h a v e p r i o r i t y s t a t u s , a n d t h e s o l u t i o n i ssub jec t to the to p p r io r i ty. In th i s ca se , the v i sua l access( t h e s e c o n d c o n s t r a i n t ) h a d p r i o r i t y o v e r s y m m e t r y.T h e r e f o r e , t h e s y m m e t r y c o n s t r a in t w a s r e l ea s e d t oc o m p r o m i s e t h e s o l u t i o n .

S e a r c h

Reco gnition a nd presolution models

W h e n k n o w l e d g e i s r e p e a t e d l y u s e d i n d e s i g n , t h e s k i l lb e c o m e s m o r e a n d m o r e r a p i d a n d a u t o m a t i c . W h e n ask i l l ge t s to an au tom a t ic l eve l , i t r equ i re s l e s s a t t en t iona n d t h e p e r s o n w h o i s u s i n g t h e s k i l l m a y l o s e ab i l i ty t o

d e s c r i b e t h e s k i l l v e r b a l l y. T h i s i s t h e p h e n o m e n o n o fre t r i ev ing a p re so lu t ion mode l fo r r ecogn i t ion sea rch .S ince the in fo rm a t ion i s a pa r t o f a p re fab r ica ted so lu -t i o n , w h e n e v e r a s t i m u l u s p r e s e n t s , t h e a n s w e r i s i m -m e d i a t e l y f o u n d a n d s k e t c h e d . T h e r e a r e n i n e p r e s o l u -t i o n m o d e l s s h o w n i n T a b l e 7 . A s i n Ta b l e 7 , s e v e n o u t o fn i n e p r e s o l u t i o n m o d e l s a r e i c o n i c i m a g e s , a k i n d o fi n t e r n a l re p r e s e n t a t io n . I n o t h e r w o r d s , t h e s u b j e c t c a nd r a w t h e m i m m e d i a t e l y u s i n g a v e r y s i m p l e s k e tc h .T h u s , w h e n a d e s i g n u n i t i s a c t i v a t e d i n s h o r t - t e r mm e m o r y, t h e s u b j e c t c a n r e tr i ev e t h e i m a g e q u i c k l y. T h i ssugges t s tha t a so lu t ion fo r a des ign un i t i s con s t ruc ted in

a d v a n c e a n d c a n b e r e p e a t e d l y a d a p t e d t o t h e n e e d s o fTa b l e 7 . P r e s o lu t io n m o d e l s u s e d b y t h e s u b j e c t

Design unit Presolution model Typ e

Building massDoric co lumn

Doric c o l u m nBathroom,staircaseWorkshopPorch ,workshop roofGarageStaircase

Workshopwindow

A two-storey buildingA setting on a stage,a free-standing elementA column supports ceiling

A rectangular patternA sunken courtyard

Pitch roofTh e shape o f garage is a shade likeA pro totypica l image o f staircaseplanA pro totypica l image o f clerestoryelev.

Concept

ImageImage

ImageConcept

ImageImage

Image

Image



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new c i rcumstances , and tha t the image i s encoded inme m o ry b y a v e ry s p e ci al c o d es~ . The res t o f the mo delsa re used fo r des ign ing new ob jec ts . The under ly ingthought process is to retr ieve a presolution model f irs t ,then to evoke the assoc iated des ign ru les fo r the com ple-t ion o f the so lu tion . T hese ru les a re in i tia lly em bed ded a tthe moment when the p reso lu t ion model was fabr ica ted

in the pas t exper ience . Thus , a p reso lu t ion model i s apar t o f the des ign schemata , and has the icon ic imagena ture .

Generate-and-test

Solu t ions o f a des ign un i t a re genera ted b y in t roduc ing ades ign cons t ra in t . Two phenomena have been iden t i f iedin the generate-and-test cycle .

I f a so lu t ion has been genera ted bu t no new des ign

cons t ra in t i s evoked , then no tes t i s ca l led . T he sys temmoves to the percep tua l - tes t , look ing fo r the nex tdesign unit .

I f a so lu t ion has been genera ted and a new des ignconstraint is act ivated, then a test fol lows. If the testsucceeds , then the sys tem proceeds to the nex t des ignunit . I f the test fa i ls , then the system will search fornew cons t ra in ts to genera te a new so lu t ion on the nex tgenerate-and-test cycle . A clear example is duringep isode 4 under the goa l o f so lv ing the Dor ic c o lumnand the ba y window . The sub jec t app lied the p reso lu-t ion model to genera te a so lu t ion and used a cons t ra in t

to test the result . Such a generate-an d-test cycle wasrepeated twice for two al ternatives. Finally, the sub-ject pulled three design constraints (visual focus,suppor t fo r ano ther e lement , loca t ion o f the co lum noutside) to generate a f inal result . This suggests thatthe genera te -and- test m ethod i s a sea rch cyc le .

An in te res ting ques t ion , w hich has no t been posed up toth is po in t , i s wha t happen s to the sys tem i f mo re than o nesolution is generated? Theoretical ly, solutions wil l betes ted by o ther cons t ra in ts to de te rmine the feas ib le one .How ever, tha t w as no t the case g iven the da ta f rom th is

exper iment . For example , the sub jec t re t r ieved the con-straint that ' a fu 'eplace is a place where people s i taround' . Two alternative solutions sat isf ied the con-straint . The subject used the test constraint (c irculat ion)to test only one solution and reached the f inal decision.The da ta does no t show tha t the sub jec t tes ted the o thersolution. A better interpretat ion for this example is thatafter the generation of solutions, perceptual- test a lsoperce ives the po ten t ia l o f the so lu t ion and de te rmines thesolution path. In this case, the perceptnal- test perceivedtha t one so lu t ion was more p rom is ing , so tha t the o therso lu t ion was abandoned wi thou t tes t .

M eans-end analysis

Besides the recognit ion and generate-and-test search

m etho ds, the sub ject a lso used m eans--end analysis in thisexper iment . An ex ample shown in Tab le 8 i s c i ted f romepisode 5. In this exam ple, the goal of the episode was todevelop an ini t ia l space layout. The design unit was astaircase. The subject indicated that he was looking for ak ind o f geom etry ( subgoa l) to f i t in to the se rv ice bay o fthe s ta i rcase and the ba th room. Af te r the subgoa l had

been iden t i f ied , he f i r s t used a p reso lu t ion model togenera te a so lu t ion , which fa i led when tes ted . Then hesearched for seven rules (operators) for sequential movesin order to arr ive at the goal s ta te , which is the typicalme thod of me ans-en d ana lysis . F ro m observ ing theprotocol data , i t is reasonable to say that the generate-and- tes t sea rch method was used to search fo r one ru lefor the generator and another rule for the test , while theme ans-en d ana lys is was used to search fo r the whole seto f ru les.

I n i t ia l p r o b l e m s p a c e a n d t a s k i n s t r u c t i o n

N ew er a nd S imon suspec t tha t the in i tia l p rob lem spaceis e i ther ready-made or i s cons t ruc ted f rom dements inthe long- te rm me mo ry o f the p rob lem so lver 11. The f i r step isode o f th is exper iment revea led tha t p ro to typ ica lcons t ra in t schemata were ins tan t ia ted f rom memory.Since the value of the instantia ted c onstraint sche mavaried from task to task, data input was needed to f i l l upthe s lo t. Th is exp la ins why da ta inpu t occurred th rough-ou t the f i r s t ep isode ( see p rob lem behav iour g raph) .

The des ign p rogramme prov ides a cover s to ry which

implici t ly or explici t ly specif ies what the designer mustcons ider. How ever, a p rob lem so lver may have d i ff icu l tyunders tand ing a task p resen ted th rough na ture languageins t ruc t ion ss. T he in format ion ex t rac ted f rom the task-unders tand ing per iod de te rmines the represen ta t ion tha ta des igner has . For example , the workshop in th isexper iment was to be a p rofess iona l workshop . W hi le theinstruction indicated th e occup ation of the cl ient , i t d idno t spec i fy the na tu re o f the worksh op . T hus , the sub jec tmis taken ly perce ived i t in h is own wa y and represen ted i ta s a h o b b y w o rk s h o p .

I N VA R I A N T S T R U C T U R E A N DS I M U L AT I O N M O D E L

I nva r i an t s t r uc t u r e

Results discussed in previous sections confirm the exist-ence o f a goa l stack , the des ign schemata , the percep tua l -tes t and the search methods used by the sub jec t . Basedon the f indings, an invariant s truc ture o f cognit iveprocesses i s ma pped in to the p roposed m odel and shownin F igure 4 . Th is cogni t ive model can be b r ie f ly de-

scribed as fol lows.When a goa l i s deve loped , a des ign un i t and the

assoc ia ted schema a re re t r ieved f rom long- te rm m emo ry.The re t r ieved schemata spec i fy the cur ren t goa l. Th is i sthe p rob lem s t ruc tu r ing s tage. Th en the sys tem searches

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Ta b l e 8 . E x a m p l e o f m e a n s - e n d a n a l y s i s s e a r c h m e t h o d

Pro toco l t ranscr ip t Search metho ds Draw ing resu l t

# 9 7 : Uh m , I a m t r y in g to t h in k o f so m e k in d o f f a c - Su b g o al : d e v e lo p a g e o m e t r y 1 / Ito r, o f geom etry, [d raw a rec tangu lar, d iv ide i t Goal spec i f ica tion : the geom etry sho u ld g ivein to th ree par ts , d raw the r igh t end par t as s ta ir- some basic o rgan iza t ion in which a serv icecase] , which g ives me some basic o rgan iza t ion , bay cou ld f i tin which it cou ld f i t in . . . se rv ices such l ike a Design un i ts : ba th room and s ta ircase

# 9 8 :

# 9 9 :

# 1 0 0 :

# 1 0 1 :

# 1 0 2 :

# 1 0 3 :

# 1 0 4 :

# 1 0 5 :

# 1 0 6 :

# 1 0 7 :

serv ice bay tha t cou ld f i t in , which a lso g ives ,(47:08)

The en t rance [d raw a l ine be low the s ta i rcase]and the house , I don ' t wan t th is so lu t ion , th is ,(47:52)

On e o ther th ing i s tha t typ ica l , jus t pu t the ser-v ice bay, bas ica lly the b a th ro om and s ta ircase , inthe same bay, and tha t migh t g ive the d iv is ion ,le t the l iv ing and l iv ing , d in ing , and k i tchen ,a n d wo r k sh o p o u t s id e , a n d th e n y o u wa n t t okeep these th ings toge ther, k i tchen and d in ingand then poss ib ly, d in ing and l iv ing toge ther.(48:06)

T h e th in g th a t I a m t r y in g to d o i s . . . t o (4 8 :3 7 )

ge t the ba th roo m, s ta i rcase and the k i tchen . Be-cause these are basically the service spaces.(48:48)

An d a c lear o rgan iza t ion , which w ould a lso g ivethe k ind o f d iv ision , tha t I w an t fo r l iv ing andd in ing , and these th in ws a re no t o n ly fo r th isf loor, l ike I sa id before , i f I am go ing to p laceb e d r o o m s o n u p p e r f l o o r, o n t h e se c o n d f lo o r,then these th ings have to con t inue on the secondf loor. So i t i s no t on ly in th is f loor tha t I amth ink ing abou t , bu t i s a lso , where the spaces tha ti s go ing to be f i t ted o n the u ppe r f loor layou t aswel l , tha t I cou ld con t inue th is same ba th roomupsta i r s , the s ta i rcase obv iously wi l l be go ingone mo re f loor, so tha t ins ide to leave the sam ev o lu m e o p e n u p o n u p p e r f l o o r s . So to g e th e rwi th r o o m s a n d sp a c es o n th e g r o u n d f lo o r, t h eya lso go ing to be a t leas t par t ia l ly car r ied fo rwardo n u p p e r f l o o rs , so th e se b o th t h in ~ a r e t h a t Iam g o ing to match . (49 .03)

An o th e r p o ss ib i li t y i s i n wh ic h I p l a c e . . . [ d r a wa rectangular] (50:28)

Sta i rcase a n d . . . [d raw s ta ircase ins ide the rec-tangular] (S0:40)

Serv ice fo r the ba th ro om [draw an o ther b lock ontop] , take these [ stai rcase and b a th room ] as veryimportant [space] (51:04)

W hich I have to leave , (51:28)

An d . . . t h a t le a v e s a n o th e r c i r cu l a ti o n z o n etha t has to b e car r ied fo rward a t leas t un t i l here[d raw a ver t ica l l ine nex t to the b lock] , and y oua ls o n e e d a d o o r, t h a t g o e s . . .( 5 1 : 4 2 )

Recognition + Generate-and-testPreso lu t ion model : a rec tangu lar fo rm for se r-

v ice bayGenera te : a geometry o f th ree bays , one fo r

staircaseTest const ra in t : the d is tance to en t rance . Test

fails

Mea ns-end analysisRule 1 : pu t the serv ice bay, the ba th ro om and

sta ircase in the sam e bay

Rule 2 : leave l iv ing , d in ing , k i tchen and work-

shop ou ts ideRule 3 : keep k i tchen and d in ing toge ther, keep

d in ing and l iv ing toge ther

Rule 4 : ge t the serv ice spaces which a re ba th -room , s ta ircase and k i tchen

Rule 5 : i f b e d r o o m s a r e p l a c e d o n u p p e r f l o o r,t h e n b a th r o o m a n d s t ai rc a se h a v e to b econ t inued on second f loor

Ru le 6 : o p e n b o th v o lu m e o f b a th r o o m a n dsta i rcase up on upper f loor

Rule 7 : leave a c i rcu la tion zone

Subgoal ach ieved , p rob lem so lved

[ I

!

I

I |

I

f o r r u l e s e m b e d d e d a t t h e s c h e m a f o r s o l u t i o n g e n e r a -t i o n . I f th e s o l u t i o n i s g e n e r a t e d a n d a n o t h e r s c h e m a c a nb e e v o k e d , t h e t e s t p r o c e e d s . T h i s i s t h e p r o b l e m s o l v in g

s t a g e. T h e p e r c e p t u a l - t e s t w i ll c o n tr o l t h e s y s t e mw h e n e v e r t h e f a i l u re i n m e m o r y r e t r i e v a l o r i n s e a r c ho c c u r s .

T h i s c o g n i t iv e m o d e l h a s a g o a l - d r iv e n b u t p e r c e p t u a l -t e s t o r i e n t e d n a t u r e .

S i m u l a t i o n m o d e l

I n o r d e r t o t e s t t h e a c c u r a c y o f t h e m o d e l b e i n g d i s c o -

v e r e d , a f r a m e w o r k f o r s i m u l a t i n g t h e w h o l e c o g n i t i v ep r o c e s s i s p r o p o s e d b e l o w. T h i s s i m u l a t i o n m o d e l i ss i m p l i f i e d o n a c o n c e p t u a l d i a g r a m i n F i g u r e 5 . T h ef u n d a m e n t a l s t r u c tu r e c o n t a in s l o n g - t e r m m e m o r y ,s h o r t - t e r m m e m o r y , p e r c e p t u a l -t e s t a n d t h e s e ar c h

Vol 11 N o 2 April 1990 73


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G

I G o a l p l an

M e m o r y

I K n o w l e d g eb a s eI D e s ig n n it I ~ ~ [ Datainput I

I S c h e m a ta I

~ \ g o a l ~ a d e s ~ ~ soh io n

Figure 4. Cogn itive mode l of design problem solving

me c h a n i sm. T h e lo n g -t e rm m e mo ry is re p re s e n te d b y al is t o f schemata and an in te rna l represen ta t ion o f aknowledge ne t . The work ing memory represen ts shor t -t e rm m e mo ry. T h e b lo c k o f p ro d u c t io n s y s te m in c lu d e sthe con t ro l s t ra tegy and search mechan ism. This modelin tends to p rov ide the capab i l i ty o f represen t ing the

invariant cognit ive s tructure and to f i t the protocol dataas well .

Sc h e m a r e p r e se n ta t ion

In the model , the long- te rm memory i s represen ted bytwo e lements , the in te rna l represen ta t ion and the sche-mata . A schem a con ta ins iden t if ie r, a rgumen ts and ru les.Th e templa te o f a schema is shown as:

< I d e n t i f i e r > < A r g u m e n t s >R u l e s : I f . . .

t h e n . . .The iden t if ie r is the nam e tag o f the schema. Theargum ent i s the des ign un i t. The ru le can be coded by an

S c h e m a t a

~ l I n t e r n a lI repres en ta t ion

Product ionsystem

W o rk in gm e m o r ye l emen t s

er n a m [Y I represen ta t ion

Figure 5. Concep tual diagram of simulation model

i f - then c lause where a body o f des ign knowledge i sem bedd ed . Th is i f - then c lause a lso has the na tu re o f aproduc t ion . There fore , the condi t ion s ide cons is t s o fdec la ra t ive knowledge and the ac t ion s ide has the p ro-cedura l knowledge . In re fe r r ing to Tab le 6 , fo r example ,the p ro toco l s ta tements can b e conver ted in to ru le repre -

sen ta tion and in tu rn can be represen ted b y schemata ( seeTable 9 ) . Each schema can a lso be conver ted in to a LIS Pfunction. In doing so, the identif ier is the nam e of thefunc t ion , and the a rgument i s the var iab le . Whenever afunc t ion i s evoked , i t i s eva lua ted . T he va lue o f thefunc t ion i s then re tu rned and bound to the in te rna lrepresentat ion. The internal representat ion is a l is t ofl i teral ized declarat ions specifying the relat ions of designunits .

Contro l s t ra tegy

The produc t ion sys tem con t ro ls the p rogress o f thesys tem. A f i r ing o f a p roduc t ion depen ds upo n thee lement tha t appears in work ing memory. The work ingm em ory c ontains a set of l i teral ized declarat ions of goaland constraint . Th e d eclarat ion of a goal consists of theidentif icat ion na m e and a vector of design units that areman ipu la ted und er the goa l . The dec la ra t ion o f thecons t ra in t cons ist s o f the na me of the cons t ra in t and thenam e of the des ign un i t to which i t i s boun d . W heneve rthe work ing me mo ry e lement matches the condi tion s ideof the p rodu c t ion , the co r responding schema is evokedand evaluated. When a solution is generated, then i t isd rawn as an ex te rna l represen ta t ion .

In o ther research s tud ies, the p rodu c t ion sys tem isused to encode bo th the dec la ra t ive knowledge andprocedura l knowledge . For example , the same se t o f

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Ta b l e 9 . S c h e m a r e p r e s e n t a t i o n a n d L IS P f u n c t i o n

Schema representation ()

Rule = If there is a then put building at

Rule = If (< A> ) is private

and () is privateand and are adjacentthen the com er formed by A and B is a private co mer

()Rule = If x = next-to-property

then it is private

L IS P representation(putpro p East 'private-property 'next-to)(putprop North 'private-property 'next-to)(putprop West 'road 'next-to)(putprop South 'road 'next-to)

(defun private-edge (edge)

(cond ((equal (get edge 'next-to) 'private-property) 'private)))(defun private-comer 0

(prog (comer)(setq comer '((North East) (South East) (North West) (South West)))

loop(cond ((null (caar comer)) (return nil))

((and (equal (private-edge (caar comer)) 'private)(equal (private-edge (cadar comer)) 'private))(return (car comer))))

(setq comer (cdr comer))(go loop)))

(defun site-privacy (building)(setq building (private-comer)))

p ro toco ls o f schemata i s conver ted d i rec t ly in to ' p roduc-t ion rule s ' , o r ' i f - th en ' rules 39. To do so, a s imple set ofconvers ion ru les can be used , such as when the p ro toco lsma n i f e s t a n i f - t h e n , i f -w h e n o r w h e n - th e n s t ru c tu r e .This t ransformat ion i s qu i te s t ra igh t fo rward an d covers amajor i ty o f the p ro toco l da ta. In th is research , theproduc t ion sys tem was main ly used to map the con t ro lsys tem. By do ing so , the domain knowledge was ab le tobe d i ffe ren t ia ted . Th e purpos e was to c la r ify the cogni -t ive s t ruc tu re and the dom ain know ledge , so tha t themodel cou ld s imula te the cogni t ive model wel l . Thedeve loped produc t ion sys tem is shown in Tab le 10 .

S P E C IF I C F I N D I N G S A N D C O N C L U S I O N

There a re 286 to ta l moves in the p rob lem behav iourgraph . T he co l lec ted da ta o f opera to rs and the num ber o fcor responding m oves a re l i sted in Tab le 11. The ca tegoryof d raw ac t ion in th is tab le mea ns tha t the sub jec t t racedthe o ld d rawings and tha t i t was a mechan ica l motora c t io n . T h e e mp ty mo v e h a p p e n e d w h e n th e s u b je c tverba l ized e i ther the nam e o f the des ign un i t o r the nam eof the cons t ra in t, bu t no dec is ion had been m ade . T h iswas in te rpre ted as the sub jec t t r ied to scan th rough theknowledge base to evoke appropr ia te in format ion . Ex-t rac ting the n um ber o f moves o f these two ca tegories

f rom the to ta l , the re were 210 moves invo lved wi th theapplication of c lassif ied operations. W ithin th em , theapplications of identif ied constraint schem ata consti tute171 moves . In o ther w ords , 81 .4% o f the mov es werecaused by the app l ica t ion o f cons t ra in t schemata . Th isresult suggests that design constraint is a ma jor mea ns fordes ign p rob lem so lv ing.

Presumably, the des ign p rob lem so lv ing ab i l i ty i sdec ided by the fac to r o f the num ber o f cons tra in ts ,assoc ia ted ru les , and preso lu t ion m odels s to red in long-te rm mem ory. T hus , a la rger num ber o f these fac to rs w i llenhance the des ign sk il l. T he con ten ts o f schemata haveb e e n u s e d b y C h iet a l . to s tudy the d i ffe rences be tw eenexper ts and nov ices in so lv ing phys ics p rob lem s4. Theyfound tha t the schem ata o f the exper ts con ta in m oreprocedura l knowledge . Th is exp la ins why ru les in sche-mata de te rmine the des ign ab i l i ty. The des ign ab i l i ty i sa lso de te rm ined by the fo l lowing two fac to rs .

Th e abi l i ty of selecting rules in const raint schema ta.I f therules in a schema are insuffic ient for solution genera-t ion , then o ther ru les mus t be se lec ted . For example ,c l imate was a g loba l cons t ra in t which had been in -s tan t ia ted ea r ly a t the fws t ep isode and was used fo rdec id ing the window loca t ion on sur faces . As shownin the p ro toco l , the sub jec t d id no t have an app ropr i -a te ru le fo r dec id ing which o r ien ta tion shou ld be used

DESIGN STUDIES 75


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Ta M e 1 0 . Te m p l a t e o f p ro d u c t io n s y s te m s

P I : S y s t e m h a l tsI f c u r r e n t g o a l = n i l

and f i rs t (goal) = ni land s ta t e = do ne

then ha l t ;

P 2 : S y s t e m d r i v e r

I f c u r r e n t g o a l < > n i land f i r s t (goa l ) < > n i la n d s ta t e = d o n e

t h e n c u r r e n t g o a l = p o p ( f a s t ( g o a l) )and s t a te = n i l ;

P3 : In i t ia l sys t emI f c u r r e n t g o a l = n i l

and f i rs t (goal) < > ni land s t a t e = n i l

then cu r ren t goa l = pop ( fas t (goa l ) ) ;

P 4 : I n s t a n t ia t e s c h e m aI f c u r r e n t g o a l = G

a n d d e s i g n u n i t = D

t h e n r e t ri e v e c o n s t r a i n t s c h e m a ;P 5 : S c h e m a r u l e a p p l i c at i o n

I f c u r r e n t g o a l = Ga n d d e s i g n u n i t = Da n d c o n s t r a i n t s c h e m a = C land c ond i t ion (C1) = n i l

then re t r i eve o ther subschema to f i l l up cond (C1) ;

P 6 : D a t a i n p u tI f c u r r e n t g o a l = G

a n d d e s i g n u n i t = Da n d c o n s t r a i n t s c h e m a = C Iand cond i t ion (CI) = n i land no o ther schema i s ava i l ab le

t h e n s u b g o a l = d a t a i n p u t ;

P 7 : A p p l i c a t i o n o f p r e s o l u ti o n m o d e lI f c u r r e n t g o a l = G

a n d d e s i g n u n i t = Da n d c o n s t r a i n t s c h e m a = C la n d t h e r e i s a p r e s o lu t i o n m o d e l

t h e n a p p l y t h e p r e s o l u d o n m o d e l f o r D ;

P S : C o n s t r a i n t s c h e m a a p p li c a ti o n f o r g e n e r a t i o nI f c u r r e n t g o a l = G

a n d d e s i g n u n i t = Da n d c o n s t r a i n t s c h e m a = C I

then va lue (C1) = eva lua te Cla n d b o u n d v a l u e to D ;

P9 : So lu t ion t es t ingI f c u r r e n t g o a l = G

and des ign un i t = Da n d s o l u ti o n ( D )and there i s cons t ra in t s chema = C2and so lu t ion (D) i s conf l i c t to ru l e (C2)

then tes t fai lsa n d s o l u ti o n ( D ) i s a b a n d o n e d ;

P I 0 : I m a g e u n i tI f c u r r e n t g o al = G

and des ign un i t = Da n d D i s a n im a g e u n i t

then subgo al = so lve Dand push (cu r ren t goa l (goal ) )and cu r ren t goa l = subgoal ;

P l l : R e - e v a lu a t e a d e s i g n u n i tI f c u r r e n t g o a l = G

and des ign un i t = Da n d D h a s b e e n m i s ta k e n l y i n te r p r e t e d

then subgoal = so lve Dand push (cu r ren t goa l (goa l ) )a n d c u r r e n t g o a l = s u b g o a l ;

P 1 2 : S u b g o a l d e v e l o p m e n tI f c u r r e n t g o a l = G

and des ign un i t = Dand so lu t ion (D)and g loba l cons t ra in t (D) = GC1and so lu t ion (D) i s conf l i c t t o ru l e (GC1)

then subgo al = so lve Dand push (cu r ren t goa l (goa l ) )and cu r ren t goa l = subgoal ;

P13 : Process to nex t des ign un i tI f c u r r e n t g o a l = G

and des ign un i t = D1and so lu t ion (D1)then des ign un i t - - re t r i eve nex t des ign un i t D2 ;

P 1 4 : Te r m i n a t i o n o f c u r r e n t g o a l s ta t eI f c u r r e n t g o a l = G

and des ign un i t = n i land cons t ra in t s chema = n i l

then s t a te = do ne ;

Ta M e 11 . N u m b e r o f m o v e s b y o p e ra t o r c a te g o r y

O p e r a t o r c a t eg o r y N u m b e r o f m o v e s

D a t a i n p u t f o r d e s ig n u n i tD a t a i n p u t f o r s c h e m a i n s t a n ti a ti o nRule app l i ca t ion fo r schema ins t an t i a t ionA p p l i c a t i o n o f c o n s t r a i n t s c h e m a t aA p p l i c a t i o n o f i n s t a n ti a te d s c h e m a v a l u eP r e s o l u t i o n m o d e l

D r a w a c t i o nE m p t y m o v e

A p p l i c a t io n o f u n i d e n t if i e d s c h e m aM i s s i n g d a t a o f s c h e m a a p p l i c a ti o n

To t a l :

31812

129129

5719

8

19

286

t o p la c e t h e w i n d o w a n d w h i c h s u r f a c e s h o u l d b ea v o i d e d h a v i n g g l a z in g . H e n c e , t h e w i n d o w l o c a t i o no f t h e l i v in g r o o m a n d k i t c h e n w e r e n o t r e s o lv e d .H o w e v e r , t h e s u b j e c t w a s a b l e t o re t r ie v e a n o t h e rr u l e , w h i c h w a s t o r e d u c e t h e w i n d o w a n d t h e g l a z in gs iz e , a n d a n a l t e r n a ti v e s o l u t i o n w a s r e a c h e d .The ability of developing new constraints for the test of anewlyg e n e r a te d de s /g n u n /t . A n e w l y g e n e r a t e d d e s i g nu n i t m a y n o t b e t h e o n e s t o r e d in t h e k n o w l e d g e b a s e.I f it is a n e w f o r m , t h e n t h e a b i l i t y o f a s s o c i a t i n g

e x i s ti n g s c h e m a t a t o i t, o r d e v e l o p i n g a n e w s c h e m af o r i t , w o u l d s t r e n g t h e n t h e d e s i g n s k i ll . S u c h a sk i l l i se s p e c i al l y im p o r t a n t f o r t e s ti n g t h e s o l u t i o n b e i n gg e n e r a t e d . S e v e r a l e x a m p l e s o c c u r r e d d u r i n g t h ise x p e r i m e n t .

7 6 D E S I G N S T U D I E S


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Taskunderstanding

Image o f siteorganization

Image of two-storeyhouse

Image un~

Initial spac elayout

Workshoplocation

Initial spac elayout

Workshop

Rooms ize

Space

generation

RC1 GC1 GC2 GC3 ~ RC3 RC4 RC5O? . ? O ? O ?

G D 01 G D 0 2 R D 0 3 3 R D 0 0 1 G D 0 1 G D 0 2 R D0 22 R C7 _RCl RCl RC1 C 2,G C 4 RCl RC1 RC6-NC~P'--?~ U

/G-ND1, NC l -NC 2 -NC 3RC8 RD 00 1 RDO01 RDQ01 RC1 NC6 RC8 oRDOO2oREXT22oGD07

o ~ o o$1 . RC 8,NC2 GC1-NC6 NL,~

NC3-NC5 GC5-NC7 /

S GDO 60 N D3 0(D oric column and baywindowAIt 1)I R Cl0, S2 RC~ 1FrJ G-ND3

~ >Goo6 o No4 o (A, 2 )S3/G-ND4 RC t 1 [

RC12fF_ GD06 ND5 (Alt 3)

GD06 ND6 R D061 ND7(AIt 4) & GD06 O N,D 6 0 O O(glazing)RC10 RC 13 araw I/G-ND7 RC 15/T

RC14/G-ND6 /

ND6 O R C 16_ ND6 ND6( g o a ldraw ? ~ ~ n e d )

~G D0 2 ~ GD01 oRDO02 REX)02O N D 8 0 (subgo~devek)ped)18 RC 19 NC2 S4/G ?$ 5 R C 22J T (Geometryol serv ice bay)

- N C 8 - N C 9 - N C 1 0 -N C 11 / /G -N D 8 aDO02

- R C 2 3 1/G-ND9GD01 ~ GD01 O ND10 O(Courtyard)

RC24?-NCt2"J" NC 12 RC 28/1"(subgoa! developed)RC251-NC13J GC 4 $6RC26?-NC14 ItG-NC~0RC27?-NCt5(L GI~ t _ NC12 O

l NC12 - RC 26,RC27| R C 22 /G-ND11

GD01 = RC 29 ~(workshop alt1)RC 29 "~G-ND12 (Half-sunken)

ND13J

& GD2 o" 2 o GD0~ O GD03 O G ~ O[RC~ 91 G NC~ 6 / Q NC 2 1 a NC~ 0 1 0 .C ~ . G/& G D o ~ 0 . 0 0 ~ 0 N C ~ 4 0 G D 01 O N D 1 3 O

.C26 , .C27 S71G RC 30rr .C30?-NC17/G-RD100 /G-ND15/G-NC14 (AIt 2workshop solved)

(Studio ype)GD05 _ GD04 GD 03 GD 02 GD 09 GD01 O GD10 O GD12 O GD08 O R D0010 GDO20R 0 27 ? R D 1 0 3 R D 1 2 3 R D 0 8 3 - N C 2 5 - N C 2 6

- N C 1 8 - N C 1 9 - N C 2 2 - N C 2 3 - N C 2 3 R D 0 9 2 G C 1/T-NC24

GD 02 R DO 02 G D 01 G D 0 5 G D 0 4 G D 0 3 R D0 33 R D 02 2 GD 0 7 R D 02 2 R D0 33 R D0 22 R D0 22 2 . . . . . .' O O O O O O O O O C ~ 3 O (~-ntrancesolvea)NC21/G ND13/G N C 22 ND 9/G NC 19/G NC20tG RC1 / RC1 RC,31, RC32 /G-NC18 GC5/GNC16/G /G-ND16J -NC27

/ N D9 0 G D03 GD 02 G~ O GD01 O R D0110 GD04 O (1s t l)f R C 33 ~ R C 1/T R C 1/T R C 26 /T R C 1/ /G-ND18 RC 26/T (2nd fl)

0 GD09 GD01_ GID06 GD07O- N C 2 8 - N C 2 9 - N C 3 0 /G -N C 1 9 /G .N D I~ ~ ' ~ - - " u ' - - ~ R C 2 4 - N C 3 5

RC4-NC31 RC5-NC 32 - NC33

F igure 6 . A n example o f p rob lon beha~ Tur r aph

o A f t e r t h e s u b j e ct d e v e l o p e d a c o m b i n e d i m a g e o ft h e D o r i c c o l u m n a n d t h e b a y w i n d o w , h e b e c a m ea w a r e th a t s i m p l e s h a p e a n d c o n s t r u c t i o n e x p e n s e sw e r e c o n s t r a i n t s fo r t h e n e w i m a g e .

o W h e n t h e s u b j e c t c o m b i n e d t h e c o n s t r a i n t s o fh a v i n g v i s i t o r s w i t h t h e b a s e m e n t l o c a t i o n f o r t h ew o r k s h o p , h e g e n e r a t e d a h a l f - l e v e l s u n k e n w o r k -s h o p a n d w a s a b l e t o d e v e l o p l i g h t a n d n o i s ec o n s t ra i n t s f or t h e n e w d e s i g n u n i t .

o Af t e r t h e sy mmet r i ca l d i sp o s i t i o n co n s t ra i n t h adb ee n ap p l i ed , a c en t ra li Ted k i t ch en d o o r wa s g en e r-a t e d . H o w e v e r, t h e c e n t ra l iz e d k i t c h e n d o o r w a sa l so su b j ec t ed t o t wo co n s t ra i n t s : t h e k i t ch en d o o rs h o u l d n o t b e v i s u a l l y a c c e s s i b l e f r o m t h e l i v i n gr o o m , a n d t h e p o s i ti o n o f s w i n g o f t h e d o o r s h o u l dn o t d i s t u r b t h e u s e r.

I n c o n c l u s i o n , t h i s s t u d y c o n f t r m s t h e e x i s t e n c e o f g o a l

Vol 11 N o 2 Apri l 1990 77


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Tab le 12 . Index o f des ignu n i t fo r p rob lem b ehav iour g raph

First floor

Index of GD Design units Index of RD

Building mass RD001

GD01 Workshop

window RD011wall RD012roof RD013

GD02 Living roomwall RD020window RD021entrance RD022entrance door RD0221steps RD0222fireplace RD023chimney flue RD0231chimney RD0232

GD03 Dining room

wall RD030window RD031door RD032porch RD033porch door RD0331

GD04 Kitchenwall RD040window ~X}41door RD042range, sink RD043

GD05 Bathroomwall RD050door RD051tub, sink, WC RD052

GD06 Doric columnglazing RD061

Staircase RD002landing RD0021

GD07 Bay windowglazing RD071

Second floor

Index of GD. De si gn nits Index of RD

GD08 Master bedroomwall RD080window RD081door RD082double bed RD083

GD09 MB bathroomwaft RD090door RD091dressing room RD092wall RD0920door RD0921

GD10 Bedroom Awall RD100window RD 101door RD 102bed, study, wardrobe RDI03

GD11 Bathroom Awall RDll0window RDll01door RD 111

Dressing room A RD112wall RDll20door RDll21

GD12 Bedroom Bwall RD120window RD121door RD122bed, study, wardrobe RD123

GD13 Bathroom Bwall RD130door RD131

Dressing room B RD132wall RD1320door RD1321

Roof RD003watertank RD0031

Garage RD004wall RD0040door RD0041walkway RD0042car RD005driveway RD0051tree RD006

swimming pool RD007Note:

GD stands for given design units RD stands for retrieved design units

plan, the different cognitive search methods used in design.It also explains how perceptual-test controls the progressof the system. The most important phenomenon is thatthe knowledge contained in constraint schemata providesresources for solution generation and testing. Therefore,

the constraint schemata can be seen as tools for designproblem solving. The ability of organizing and applyingschemata determines a designer's design ability. Thesefindings not onl y describe the nature o f design process indetail, but have three additional meanings as well:

that togeth er they provide a basic framework for thenovice designer to use in u nderstand ing t he science ofdesign and in developing his own design ability

that the propos ed model of schema representationfurnishes a potential for representing the design

domain-specific knowledge that the metho d used in discerning the knowledge

state to compose the solution path also provides anopportunity for the study of style in design

DESIGN STUDIES 78


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32 By m e, R W 'Menta l cookery : an i l lus t ra t ion of fac tretrieval

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Cognitive Processes in Architectural Design