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Epistemological problems in the development of intelligent information systems for industrial applications
Computers ind. Engng Vol. 17, Nos 1-4, pp. 67-72, 1989 Printed in Great Britain. All rights reserved
0360-8352/89 $3.00+0.00 Copyright © 1989 Pergamon Press plc
EPISTEMOLOGICAL PROBLEMS IN THE DEVELOPMENT OF INTELLIGENT INFORMATION SYSTEMS FOR INDUSTRIAL APPLICATIONS Ojelanki
K.
Ngwenyama,
Delvin
A.
Grant
& K.
Srihar'i
Knowledge and I n f o r m a t i o n Systems Research Group School of E n g i n e e r i n g , SUNY, Binghamton, N.Y. 13901 INTRODUCTION
Artificial I n t e l l i g e n c e ( A I ) techniques have been a p p l i e d w i t h some success t o medical d i a g n o s i s [ 9 ] , and i n d u s t r i a l e n g i n e e r i n g [ 1 3 ] . However', the knowledge ' a c q u i s i t i o n b o t t l e n e c k ' remains the p r i m a r y o b s t a c l e t o b u i l d i n g e f f e c t i v e ES a p p l i c a t i o n s [ I , ~ ] . Further" a general t h e o r y of e x p e r t i s e or" 'knowledge in work' which could lend support t o the removal of t h i s o b s t a c l e has not yet emerged, but i s in progress [ 1 2 , 1 8 ] . C u r r e n t l y ES are b u i l t on the assumption t h a t the knowledge engineer' (KE) can determine a small s t r u c t u r e d and w e l l d e f i n e d a p p l i c a t i o n area, develop a c l e a r c l a s s i f i c a t i o n of r e l e v a n t concepts and their" c h a r a c t e r i s t i c s together' w i t h a set of r u l e s r e l a t i n g them. The problem w i t h t h i s assumption i s t h a t most i n d u s t r i a l e x p e r t i s e i s not w e l l d e f i n e d . A s i g n i f i c a n t p o r t i o n of the knowledge of these e x p e r t s i s d i f f u s e d , i l l - s t r u c t u r e d , u n r ' e f l e c t e d upon, and remains a t a k e n - f o r ' granted stock of s e l f - e v i d e n c e s . It
i s f a l l a c i o u s t o t h i n k of e x p e r t knowledge as c o n s i s t i n g of s t a t i c can be n e a t l y d i v i d e d i n t o r u l e s and f a c t s and captured by some knowledge r e p r e s e n t a t i o n (KR) f o r m a l i s m . This f a l l a c y leads t o the view t h a t knowledge a c q u i s i t i o n (KA) i s a t e c h n i c a l process which t r a n s l a t e s human meanings i n t o symbols which can then be operated upon by a computer" t o r e c o n s t r u c t s p e c i f i c forms of i n t e l l i g e n t human beh a v i o r . This view was successful in r e p r o d u c i n g some forms of s p e c i f i c human behavior' and p r o v i d e d the f o u n d a t i o n for' the e a r l y success of AI research ( c f . Minsky [ 1 6 ] , Newell & Simon [ I ? ] ) . Therefore i t has deeply i n f l u e n c e d many researchers in the f i e l d . This view i s too narrow because t h e r e i s l i t t l e reason t o b e l i e v e t h a t i t w i l l e v e n t u a l l y be succ e s s f u l at b u i l d i n g 'r'eal ~ ES in the domains in which ' r e a l ' e x p e r t s work, such as law, management p l a n n i n g , f i n a n c e , systems design e t c . structur'es that
OBJECTIVE OF THE PAPER
This paper--~'nquires i n t o some of the p r i n c i p a l problems of KA. I t i d e n t i f i e s t h r e e types of i m p l i c i t background knowledge t h a t e x p e r t s r o u t i n e l y and u n r e f l e c t i n g l y use in everyday problem s o l v i n g . Although t h i s knowledge i s not w i d e l y acknowledged by c u r r e n t methods of KA, i t has been found t h a t they help e x p l a i n many of these problems [ 1 8 , 2 0 ] . Current t h e o r y in ES design recognizes only two types of knowledge: f a c t s and r u l e s , q u e s t i o n s which a r i s e are whether" t h i s i s a good d i c h o tomy and whether" i t i s s u f f i c i e n t , or' do we need t o consider' other" types of knowledge? I f more types are necessary, do we need d i f f e r e n t methods of KA, or' can we r e l y on one method for' a l l types of knowledge? Research on knowledge types i s i m p o r t a n t , they d i r e c t l y i n f l u e n c e the d e v e l o p e r ' s o p i n i o n about which knowledge needs t o be a c q u i r e d and how d i f f e r e n t types of knowledge are used in problem s o l v i n g . It also discusses epistemological issues relevant to the elicitation of k n o w l e d g e (KA) f r o m e x p e r t s , and t h e i m p o r t a n c e o f t h e s e i s s u e s t o t h e development of knowledge systems (KS). It discusses the difficulties t h a t c o n f r o n t the KE when d e a l i n g w i t h e x p e r t i s e t h a t i s h e a v i l y dependent on e x p e r i e n t i a l knowledge. T h e p a p e r i s c o n c e p t u a l l y organized i n t h r e e blocks= (a) c o n c e p t u a l , (b) t h e o r e t i c a l and (c) n o r m a t i v e . Section (a) presents some b a s i c concepts of ES and knowledge e n g i n e e r i n g Section (b) focuses on e p i s t e m o l o g i c a l i s s u e s , KA problems, and on i d e n t i f y i n g p r o b l e m a t i c knowledge types which the KE must face in p r a c t i c e . Section (c) o u t l i n e s a KA s t r a t e g y for' d e a l i n g w i t h these problems. By f o c u s i n g on these kinds of i s s u e s , the paper hopes t o reveal reasons for' the apparent b o t t l e n e c k in KA and thereby s t i m u l a t e f u r t h e r ' i n q u i r y .
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WHAT ARE KNOWLEDGE SYSTEMS? KS are d i f f e r e n t i a t e d from o t h e r types of e x p e r t or' knowledge worker' support systems which are based on a l g o r i t h m s or" h e u r i s t i c procedures. One working d e f i n i t i o n of ES i s p r o v i d e d by the B r i t i s h Computer' Societ y : An ES i s regarded as the embodiment w i t h i n a computer' of a knowledge-based component, from an e x p e r t s k i l l , in such a form t h a t the system can offer, i n t e l l i g e n t advice or" take an i n t e l l i g e n t d e c i s i o n about a processing f u n c t i o n . A d e s i r a b l e a d d i t i o n a l c h a r a c t e r i s t i c , which many would consider' fundamental, i s the c a p a b i l i t y of the system, on demand, t o j u s t i f y i t s own l i n e of reasoning in a manner" d i r e c t l y i n t e l l i g i b l e t o the e n q u i r e r . (quoted by Forsyth [ I 0 ] ) .
An ES i s expected t o perform a c t i o n s t h a t would n o r m a l l y be considered human; f o r example, g i v i n g advice and t a k i n g i n t e l l i g e n t d e c i s i o n s . I t i s a l s o expected t o e x p l a i n i t s l i n e of ~r'easoning t and t r a n s f e r knowledge t o a human user' when i n s t r u c t e d t o do so. The system i s expected t o act as an i n t e l l i g e n t a s s i s t a n t t o human a c t o r s . Here i s a l i s t of t y p i cal a c t i o n s which users and KE have come t o expect of any KS: I . A s s i s t s in the s o l u t i o n of a n o n - t r i v i a l problem t h a t would o t h e r w i s e be the purview of a human e x p e r t . 2. Provides i n t e l l i g e n t advice t o the user'. 3. A s s i s t s in the e l i c i t a t i o n of f a c t s p e r t i n e n t t o the problem domain. A. Takes i n t e l l i g e n t d e c i s i o n a c t i o n s when a p p r o p r i a t e . KNOWLEDGE ENGINEERING Knowledge e n g i n e e r i n g i s concerned w i t h requirements s p e c i f i c a t i o n , des i g n , and i m p l e m e n t a t i o n of knowledge-based ES. KA i s aimed a t a r r i v i n g a t an unambiguous d e s c r i p t i o n of the contents of the knowledge-based component of the KS. Buchanan [ 6 ] d e f i n e s KA as ' t h e transfer" and tr'ansf o r m a t i o n of problem s o l v i n g exper,t i s e from some knowledge source t o a (computer') pr'ogram. ~ For' the purposes of t h i s paper', KA r e f e r s t o the process by which the knowledge r e q u i r e d for" a would be ES t o f u n c t i o n e f f e c t i v e l y i s e l i c i t e d and s p e c i f i e d . The q u a l i t y of KA i s of c r i t i c a l importance for' b u i l d i n g successful KS a p p l i c a t i o n s because the v a l i d i t y of the c o n c l u s i o n s of a KS depends on the q u a l i t y of the premises on which they are based. .
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KA Approaches .
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Content Analysis Concept Induction Personal Construct Theory Psychological Scaling Techniques Discourse Analysis .
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Table
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Recent
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Papers .
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Kr'ippendorf 1980 Michalski 1983 Boosie 198~ Cooke & McDonald 1986 Belkin et. al. 1986 .
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Recent approaches t o KA
KNOWLEDGE ACQUISITION AND EPISTEMOLOGY Knowledge e n g i n e e r i n g i s i n e x t r i c a b l y l i n k e d t o e p ist e mo lo g y by v i r t u e of i t s o b j e c t of i n t e r e s t [ 1 8 ] . While knowledge e n g i n e e r i n g i s concerned w i t h i d e n t i f y i n g and t r a n s l a t i n g i n t o computer" pr'ocessable form the knowledge t h a t human e x p e r t s apply in t h e i r ' work, epistemology i s concer'ned w i t h questions such as: ( I ) What i s knowable? (2) What i s pr'obable vs c e r t a i n knowledge? (3) What are the c r i t e r i a o f j u s t i f i c a t i o n for' knowledge? (4) How are knowledge and human a c t i o n r'elated? These e p i s t e m o l o g i c a l q u e s t i o n s , though seemingly a b s t r a c t , cannot be ignored by the p r a c t i c e of knowledge e n g i n e e r i n g . Fr'om a knowledge e n g i n e e r i n g standp o i n t , Question I may be r e f o r m u l a t e d as two separ'ate p r a c t i c a l quest i o n s : (a) Can a l l human knowledge be made e x p l i c i t for' computer' p r o cessing? (b) Can the KE r e p r e s e n t what the e x p e r t knows and makes public? questions 2 and 3 m o t i v a t e still more p r a c t i c a l questions: ( c ) How s h o u l d t h e KE c l a s s i f y , m o d e l , and v a l i d a t e different types of knowl e d g e ? Q u e s t i o n 4 a l s o has p r a c t i c a l implications. For' e x a m p l e , how i s k n o w l e d g e used by human e x p e r t s d u r i n g p r ' o b l e m s o l v i n g ? Moreover, is human p r o b l e m s o l v i n g limited to expressible k n o w l e d g e , or' a r e t h e r e other" factors involved a b o v e and b e y o n d w h a t can be e x p r e s s e d .
A l t h o u g h much r e s e a r c h n e e d s t o be done on t h e s e q u e s t i o n s the implications s h o u l d be c l e a r ' . The p o s i t i o n one t a k e s on t h e i s s u e s i n f l u e n c e s the fundamental characteristics of theories, k n o w l e d g e and a s s u m p t i o n s upon w h i c h t h e s e l e c t i o n / d e v e l o p m e n t o f KA m e t h o d s a r e b a s e d . I n t h i s regard, it is most disturbing to find that these questions r'emain largely unexamined in the literature on KA f o r ' e x p e r t s y s t e m s .
Ngwenyamaet
al.:
Epistemologyand knowledge acquisition
PROBLEMS OF KNOWLEDGE ACQUISITION KA in pr'a~'ice ~as proven to be complex, time consuming, and often un-
successful. Several approaches have been suggested ( o f . Table I . ) . However, each s u f f e r s some of the l i m i t a t i o n s l i s t e d below. The research community is f a r from a general theory of KA, and much t h e o r e t i c a l work needs to be done. Research and p r a c t i c e in KA have continued to uncover problems and puzzles. We b e l i e v e t h a t at the heart of the matter' l i e s in the e p i s t e m o l o g i c a l questions o u t l i n e d above. These problems are widely reported in the l i t e r a t u r e and are l i s t e d here f o r the reader: I . I n a b i l i t y of subjects to a r t i c u l a t e t h e i r ' problem s o l v i n g s k i l l s . 2. I n a b i l i t y of KEs to e l i c i t i m p l i c i t knowledge. 3. Inconsistency in s u b j e c t s ' d e s c r i p t i o n of t h e i r problem solving s t r a t e g i e s . ~. Incompleteness in the d e s c r i p t i o n s of problem s o l v i n g s t r a t e g i e s . 5. I n a b i l i t y of the KS developer to understand the s u b j e c t ' s d e s c r i p t i o n of the problem s o l ving s t r ' a t e g i e s . 6. I n a b i l i t y to understand and capture the openness of problem s o l v i n g to i n v e n t i o n and novel a p p l i c a t i o n s of knowledge. THREE TYPES OF IMPLICIT KNOWLEDGE
Ngwenyama ~ a ~ ' - i d e n t i f i e d three types of i m p l i c i t background knowledge t h a t has been c o n t r i b u t i n g to the above mentioned problems. I t is important to note t h a t experts operate with two l e v e l s of knowledge: (a) Present-at-hand, t h a t which they consciously r e f l e c t on during problem s o l v i n g ; and (b) Ready-at-hand, t h a t which remains in the backgroung, u n r e f l e c t e d upon during problem s o l v i n g . For' the moment we must focus on the types of background knowledge. Together these types of knowledge take the form of a t a k e n - f o r - g r a n t e d s u b j e c t i v e reference schema t h a t enables experts t o act and i n t e r p r e t the meaningfulness or" meaninglessness of a c t i o n s w i t h i n the work environment. Embedded in t h i s schema are: ( I ) R o u t i n e / I n s t i n c t i v e knowledge. (2) Tacit k n o w l e d g e / s k i l l s , and (3) I n t u i t i v e knowledge/ knowledge of recipes. These are e s t a b l i s h e d in work r o u t i n e s or' p r e w r ' i t t e n unquestioned s o l u t i o n s c r i p t s from past experience t h a t remain u n r e f l e c t i v e l y ready-at-hand for' the e x p e r t . In p r a c t i c e the KEs and experts e x h i b i t poor' c a p a b i l i t i e s for' s u r f a c i n g , q u e s t i o n i n g , and c a p t u r i n g these types of knowledge. Routine or I n s t i n c t i v e knowledge, presents the actor" w i t h d e f i n i t i v e ~ n s to problems which are organized in the f l o w of l i v e d exper'iences, w i t h o u t him having to be conscious of them. E s s e n t i a l l y , i t is the understanding of s o l u t i o n s to everyday work occurrences or puzzles which were once problematic but have been d e f i n i t i v e l y solved by some i n a r ' t i c u l a b l e s k i l l or" a r t . This means t h a t r o u t i n e knowledge can subordinate, c o o r d i n a t e , or" even dominate other aspects of knowledge in a c t i o n - s i t u a t i o n s w i t h o u t the a c t o r being conscious of i t . The e f f e c t i v e n e s s of t h i s type of knowledge becomes s u b j e c t i v e l y c e r t a i n to the user" through r e peated success under a p p l i c a t i o n . I t is thus t r u s t w o r t h y , unquestionably r e a l i z a b l e in every p r o b l e m - s i t u a t i o n , standardized and included in the store of t a k e n - f o r - g r a n t e d elements ready to be grasped at any time f o r s o l u t i o n s to s p e c i f i c problems. Routine or' i n s t i n c t i v e knowledge may be defined as innate knowledge such as t h a t p o s t u l a t e d by Chomsky with regards to human language competence.
Chomsky claims t h a t i n d i v i d u a l s have knowledgeof language beyond t h a t which they are able to d e s c r i b e , but which they use in everyday l i n g u i s t i c a c t i o n [ 6 ] . In support of t h i s argument, he p o i n t s to young c h i l d r e n who are able to e f f e c t i v e l y construct proper l i n g u i s t i c utterances, a l b e i t w i t h some d i f f i c u l t y , f a r " beyond t h e i r ' experience or' e x p l i c i t knowledge of the language. The primary c h a r a c t e r i s t i c of t h i s type of knowledge is t h a t i t is unexplainable, but is expressed in the form of accomplishing a r e s u l t without p r i o r experience t h a t is in any way commensurate with the accomplishment. T a c i t knowledge, is a n o n - a r t i c u l a b l e s k i l l which the expert knows he or d i s t i l l e d from p r i o r ' experience and consciously a p p l i e s in problem s o l v i n g . T a c i t knowledge is t h a t which can only be t r a n s f e r r e d by exemplars (an exemplar ~s a recorded approach t h a t has been a p p l i e d s u c c e s s f u l l y to a d i f f i c u l t and evasive problem). A good exmple is the t e a c h i n g of someone how to r i d e a b i c y c l e . I t has been noted t h a t no one i s able to e x p l a i n t o a student how to r i d e a b i c y c l e . The only pedagogical technique useful in t h i s s i t u a t i o n is f o r the teacher get onto the v e h i c l e and show the student how i t is done. ~ a s
The t h i r d type is i n t u i t i v e knowledge or knowledge o_f r e c i p e s . This type of knowledge i s in o p e r a t i o n when, being faced with a new problem s i t u a t i o n , we c o n s t r u c t an ad hoc theory to help us i n t e r p r e t the s i t -
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u a t i o n and develop s o l u t l o n s t r a t e g i e s . A good example i s ' s e a t of the p a n t s ' management d e c i s i o n making, where the manager i s not i n t e r e s t e d in d e v e l o p i n g general t h e o r i e s or" any s c i e n t i f i c t e s t i n g as i s proposed by s c i e n t i f i c management t h e o r i e s . I n s t e a d , he or' she has a l a r g e 'bag of t r i c k s ' and wishes t o c o n s t r u c t an e x p l a n a t i o n of the pr'oblem s i t u a t i o n b e f o r e t a k i n g a c t i o n . Often the manager' considers the 'bag of t r ' i c k s ~ t o be v a l i d a p r i o r i , confirmed by on the job e x p e r i e n c e . I n t e r e s t is confined m ~ i n - - ~ scanning the problem space f o r c e r t a i n recognizable patterns to confirm the manager"s 'gut feeling' The power' o f i n t u i t i v e k n o w l e d g e can be seen a t w o r k i n e v e r y t y p e o f problem solving activity, from simple everyday problems to the construction of scientific h y p o t h e s e s for" t e s t i n g , Ideally this type of knowledge encompasses a corpus of well tried and a c c e p t e d c o n c e p t s ( b a g of tricks) of the experienced problem solver', Moreover, it is likely to be u n s t r u c t u r e d and h i g h l y selective, unless reflected upon i n a c o n s c i o u s m a n n e r by t h e i n d i v i d u a l . A KA STRATEGY
FOR D I F F I C U L T
INDUSTRIAL
APPLICATIONS
T n ~ s t r i a l wor'~--~ractices w h ~ e x p T i c i t definition in engineering procedure manuals, and require years of practical experience to master, can be expected to cause significant problems to KA. This is because the expert's knowledge of such procedures is usually r'ooted in vast diffused experience of working with particular operations. In 'normal working conditions' the expert knows what to do without having to reflect or" articulate the decision processes. Established work routines, pr'ewritten and unquestioned scripts provide ready-at-hand knowledge which enable the expert to perform smoothly without conscious awareness. However', difficult novel problem s i t u a t i o n s and f a i l u r e s f o r c e the e x p e r t t o r e f l e c t on h i s / h e r work r o u t i n e s [ 1 9 ] . This i s because such problems i t u a t i o n s t h r e a t e n the ' t r i e d and accepted' s o l u t i o n s t r a t e g i e s . They a l s o c o n t r i b u t e t o an i m p o r t a n t c o n d i t i o n , a ' c o g n i t i v e opening' which f o r c e s t h e e x p e r t t o s u r f a c e and s c r u t i n i z e his/her' ready-at-hand routines to ascertain w h e r e t h e y have b r o k e n d o w n , T h i s i n s i g h t has l e a d t o continuing r e s e a r c h and d e v e l o p m e n t on specific KA s t r a t e g i e s , one o f which is described b e l o w ( s e e a l s o Ngwenyama e t a l . [19]). A FOUR STAGE KNOWLEDGE A C Q U I S I T I O N STRATEGY One "&'t'ra-t-egy t K a t we have found e f f e c t i v e f o r d e a l i n g w i t h the problems of c a p t u r i n g embedded i m p l i c i t knowledge i s d e f i n e d in the f o l l o w i n g f o u r step procedure conducted w i t h two KEs and the exper't. The process i s a hermeneutic c y c l e in which the knowledge engineers must f i r s t (Stage I) o r i e n t themselves t o the a p p l i c a t i o n area. In Stage 2, the KEs enter" a d i a l o g u e w i t h the exper't. In Stage 3 case s t u d i e s of v a r i o u s l e v e l s of d i f f i c u l t y are pr'esented t o the e x p e r t for' s o l u t i o n , which the KEs w i l l observe, d i s c u s s , and analyze w i t h the help of the e x p e r t . F i n a l l y in Stage a, the h e u r i s t i c s are compiled and normalized for" coding and t e s t i n g w i t h the ES softwar'e. .
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ORIENTATION
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NORMALIZATION .
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A Four' Stage Model For" Knowledge A c q u i s i t i o n .
In Stage I the KEs should a t t e m p t t o become conversant w i t h the a p p l i c a t i o n area. Ngwenyama e t a l . [19] found t h i s t o be a c r u c i a l stage t o the success of the KA process. The f u t u r e communication and mutual unders t a n d i n g which must be f o r g e d between the e x p e r t ( s ) and KEs, hinges on how w e l l the KEs are s o c i a l i z e d i n t o the e x p e r t ( s ) ' work environment. The i n t e n t here i s not t o make the KEs e x p e r t s in the a p p l i c a t i o n area
Ngwenyama et
al.:
Epistemology and knowledge acquisition
but to provide them with the necessary base knowledge and professional language to communicate e f f e c t i v e l y [B]. Although the communication aspect has been over' looked in KA research, i t is important to remember t h a t knowledge engineering l i k e systems analysis is a social process. Rolandi supports t h i s notion; he argues t h a t the most c r i t i c a l s k i l l s of the KE are s o c i a l . Two e f f e c t i v e s t r a t e g i e s of o r i e n t a t i o n are s e l f study and p a r t i c i p a n t observation. KEs should study procedure manuals, engineering handbooks, appropriate textbooks, and reference material p e r t i n e n t to the f i e l d . P a r t i c i p a n t observation could take the form of short term apprenticeship, working along with the expert, etc. This aspect should lead to the development of mutual respect and a good rapport among the p a r t i e s , the b u i l d i n g blocks of e f f e c t i v e communication. In the d i a l o g stage the KEs conduct two types of interviews with the expert: (a) unstructured (b) structured. The unstructured i n t e r v i e w is the beginning of the e l i c i t a t i o n process and should be extensive and protracted. I t is from these interviews t h a t the KEs w i l l obtain most of present-at-hand knowledge and i n s i g h t s f o r constructing the structured i n t e r v i e w s . A large number' of the unstructured interviews need to be undertaken while the expert is performing f a m i l i a r tasks, a l l o w i n g f o r more spontaneous questioning. Structured interviews should examine s p e c i f i c problem solving routines and concepts that need c l a r i f i c a t i o n . The information gathered at the previous stages is used to construct cases of varying degrees of d i f f i c u l t y . These are then used in c o n t r ' o l l ed s i t u a t i o n s where the expert is observed throughout the problem solving process. The case technique is widely supported in the l i t e r a t u r e . Buchanan et a l . [5] have strongly advocated the use of cases as an approach to capturing the refined and subtle aspects of the e x p e r t ( s ) ' reasoning. There are two approaches to these cases that we have found successful in helping the team to surface ready-at-hand knowledge: (a) control of problem complexity, and (b) control of wor'king i n f o r m a t i o n . In the f i r s t approach, cases of varying l e v e l s of problem complexity are presented to the expert f o r s o l u t i o n . In the second approach the expert is asked to solve s p e c i f i c cases with gradually increasing l e v e l s of p e r t i n e n t information. One of the primary goals of t h i s second case approach should be to help the expert to 'breakdown' and surface takenf o r - g r a n t e d work routines. Well defined challenging case problems of various l e v e l s of d i f f i c u l t y have been found e f f e c t i v e vehicles to stimu l a t e the expert through var'~ous l e v e l s of r e f l e c t i o n on his/her work routines [19]. This approach when conceived with appropriate problems could help the KE with the e l i c i t a t i o n of t a c i t and i n t u i t i v e knowledge. The g o a l o f Stage 4 i s t o c l e a n up t h e i n f o r m a t i o n collected in the previous stages of the KA process. The main o b j e c t i v e is to i d e n t i f y and c l a r i f y and remove d u p l i c a t e ~eur'istics. I t should be conducted with the assistance of the expert. DOCUMENTATION TECHNIQUES AND TOOLS During the dialogue and case ~ s i s stages, large volumes of infor'mat i o n are produced which must be c a r e f u l l y documented f o r analysis. We found that note taking is not the most e f f e c t i v e form of documentation. Although the s i t u a t i o n improves with two KEs, i t breaks down under' the burden of the i n f o r m a t i o n . Many r;esearchers have advocated the recording of p r o t o c o l s ; we have found them i n d i s p e n s i b l e . Graphical techniques a r e also important f o r the analysis and normalization of the h e u r i s t i c s . Decision network diagrams supported by decision tables are useful, clear', concise, and are widely accepted for" i n d u s t r i a l a p p l i c a t i o n s . Although other forms of charting techniques might be superior, the most important issue should be e f f e c t i v e communication between the expert and KEs. The expert should have a f u l l reading knowledge of the charting techniques. This speeds up and enhances the quality of the analysis, and t h e r e s u l t i n g system. CONCLUSION This paper' o u t l i n e d several problems of KA which KEs encounter when d e veloping knowledge-based systems for' i n d u s t r i a l a p p l i c a t i o n s . These problems a r i s e from the c o n c e p t u a l i z a t i o n of the problem domain in which the ES is to operate and from the nature of the knowledge types which a r e a v a i l a b l e to human experts. These problems must e x p l i c i t l y or" i m p l i c i t l y be addressed by any approach to KS development. Whatever' the p o s i t i o n or' compromise adopted, i t w i l l be apparent from analyzing the design choices made, and the e f f i c t i v e n e s s of the d e l i v e r e d system.
71
72
Proceedings of the 1lth Annual Conference on Computers & Industrial Engineering
The a p p r o a c h t a k e n was t o r e l a t e t h e c u r r e n t v i e w s on t h e p r o b l e m s o f KA t o t h e k n o w l e d g e t y p e s on w h i c h human e x p e r ' t s r ' e l y when t h e y i n t e r p r e t p r o b l e m s , seek o u t r e l e v a n t i d e a s , p r o c e s s e v i d e n c e e t c . Such an a n a l y s i s s h o u l d be o f i n t e r e s t t o ES d e v e l o p e r s b e c a u s e i t h e l p s them t o understand the limitations i n t r o d u c e d by t h e a d o p t i o n o f a s i m p l e r ' u l e f a c t t h e o r y o f k n o w l e d g e . In f a c t , t h e KA b o t t l e n e c k e x i s t s because t h e r e a r e as y e t no a p p r o a c h e s w h i c h f o r m a l i z e many o f t h e k n o w l e d g e t y p e s we d i s c u s s e d . N e v e r t h e l e s s , progress in overcoming the limitations o f c u r r e n t ES d e v e l o p m e n t a p p r o a c h e s w i l l be f a c i l i t a t e d i f we a r e b e t t e r ' i n f o r m e d a b o u t why t h e p r o b l e m o f KA i s so c o m p l e x and what causes the difficulties [ 1 2 ] . Based on our' c u r r ' e n t r e s e a r c h , we have been a b l e t o d e v i s e some a p p r o a c h e s t o g e t a r o u n d t h e d i f f i c u l t T e s (one o f w h i c h we r e p o r t e d i n p a r t h e r e ) . R e s e a r c h on d e v e l o p i n g a g e n e r a l t h e o r y o f k n o w l e d g e t y p e s and m e t h o d s f o r ' KA i s c o n t i n u i n g , and has been reported in [11,19]. REFERENCES [1] B a r ' r , A . , and E.A. F e i g e n b a u m , "The Handbook o f A I " , V o l . 2,Mor'gan Kaufmann, Los A l t o s , C a l i f o r n i a , 1981.
[2]
Belkin N. d. et a l . , " E l i c i t a t i o n using discourse a n a l y s i s " , in Knowledge A c q u i s i t i o n for' Knowledge-Based Systems Workshop, Banff, Canada, Nov. 1986.
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Buchanan, B. G., Suther'land, g. L., and Feigenbaum, E. A. "Rediscovering S o m e P r o b l e m s Of A r t i f i c i a l Intelligence I n The C o n t e x t
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o f O r g a n i c C h e m i s t r y " , Machine I n t e l l i g e n c e M i c h i e , D . , 1970, p p . 209 - 25&. B u c h a n a n , B. G . , and R.O. Duda, " P r i n c i p l e s S y s t e m s " , A d v a n c e s i n C o m p u t e r s , V o l . 22, N. Chomsky, N . , " K o n w ~ d g e o f L a n g u a g e " , i n K. Minnesota Studies in Philosophy of Science, o f M i n n e s o t a P r e s s , M i n e s s o t a , 1975. C h o r a f a s , D. N. " A p p l y i n g E x p e r t Systems i n
Meltzer,
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McGraw H i l l ,
New York, 1987. Cooke N. M., and J. McDonald, "A formal methodology for' a c q u i r i n g and representing expert knowledge", IEEE Special Issue on Knowledge Representation, 1986. Feigenbaum, E., Buchanan~ G., $ J. Lederber'g, "Generality and Problem Solving: Case Study Using DENDRAL Program", Machine I n t e l l i gence, Eds. Meltzer, B., $ Michie, D., Edinburgh LTn'~v. Press, 1971. For'syth, R., "Expert Systems: Principles and Case S t u d i e s " , Chapman and H a l l , L o n d o n , 1986. K e r n , H. M., M. Schumann, "Das Ende der' A r ' b e i t s t e i l u n g " , Munchen, M u n i c h , 1986. K l e i n , H . , Lander', L . , & Ngwenyama, O . , "The K n o w l e d g e A c q u i s i t i o n
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Pr'oc. of Hawaii Conf. On System Science, Hawaii, 1989.
Kumar'a, S . R . T . , S a n j a y , J . , K a s h y a p , R . L . , Moo-'-S-o-cF~e-,C . L . , and Chang, T.C., " E x p e r t Systems i n I n d u s t r i a l Engineering", Int. Journal o f P r o d u c t i o n R e s e a r c h , V o l . 2~, No. 5, 1986, p p . 1 1 0 7 - 1 1 2 5 . [14] Krippendor'ff, K., "Content Analysis: An Introduction to its M e t h o d o l o g y " , MIT P r e s s , C a m b r i d g e , MA, 1984 [15] Michalski, R. S . , "-T-~eor'y and m e t h o d o l o g y o f i n d u c t i v e learning", i n Machine L e a r n i n g : An A r t i f i c i a l Intelligence Approach, ed., R.S~lski e t al.~--Mor'gan Kauffman P u b l . , 1983 [ 1 B ] M i n s k y , M. " S e m a n t i c I n f o r m a t i o n P r o c e s s i n g " , MIT P r e s s , C a m b r i d g e , MA, 1968. [ 1 7 ] N e w e l l , A. and H.A. Simon, "Human P r o b l e m S o l v i n g " , Prentice-Hall, N . J . , 1972. [ 1 8 ] Ngwenyama, O . , " F u n d a m e n t a l I s s u e s Of K n o w l e d g e A c q u i s i t i o n : Towards A Human A c t i o n P e r s p e c t i v e Of K n o w l e d g e S y s t e m s " , Ph.D. D i s s e r t a tion, Watson School of Engineering, SUNY, Binghamton, N.Y., 1987. [19] Ngwenyama, 0., R. Kaula, & L. Lander', "An Approach To Knowledge Acquisition For Knowledge Systems", Working Paper', 1988. [20] Ngwenyama, O. "The Knowledge Competences of Intelligent Human Action: Toward A Definition Of The UoD of Knowledge Acquisition", RVB Research Papers Vol IV, 1988.
0360-8352/89 $3.00+0.00 Copyright © 1989 Pergamon Press plc
EPISTEMOLOGICAL PROBLEMS IN THE DEVELOPMENT OF INTELLIGENT INFORMATION SYSTEMS FOR INDUSTRIAL APPLICATIONS Ojelanki
K.
Ngwenyama,
Delvin
A.
Grant
& K.
Srihar'i
Knowledge and I n f o r m a t i o n Systems Research Group School of E n g i n e e r i n g , SUNY, Binghamton, N.Y. 13901 INTRODUCTION
Artificial I n t e l l i g e n c e ( A I ) techniques have been a p p l i e d w i t h some success t o medical d i a g n o s i s [ 9 ] , and i n d u s t r i a l e n g i n e e r i n g [ 1 3 ] . However', the knowledge ' a c q u i s i t i o n b o t t l e n e c k ' remains the p r i m a r y o b s t a c l e t o b u i l d i n g e f f e c t i v e ES a p p l i c a t i o n s [ I , ~ ] . Further" a general t h e o r y of e x p e r t i s e or" 'knowledge in work' which could lend support t o the removal of t h i s o b s t a c l e has not yet emerged, but i s in progress [ 1 2 , 1 8 ] . C u r r e n t l y ES are b u i l t on the assumption t h a t the knowledge engineer' (KE) can determine a small s t r u c t u r e d and w e l l d e f i n e d a p p l i c a t i o n area, develop a c l e a r c l a s s i f i c a t i o n of r e l e v a n t concepts and their" c h a r a c t e r i s t i c s together' w i t h a set of r u l e s r e l a t i n g them. The problem w i t h t h i s assumption i s t h a t most i n d u s t r i a l e x p e r t i s e i s not w e l l d e f i n e d . A s i g n i f i c a n t p o r t i o n of the knowledge of these e x p e r t s i s d i f f u s e d , i l l - s t r u c t u r e d , u n r ' e f l e c t e d upon, and remains a t a k e n - f o r ' granted stock of s e l f - e v i d e n c e s . It
i s f a l l a c i o u s t o t h i n k of e x p e r t knowledge as c o n s i s t i n g of s t a t i c can be n e a t l y d i v i d e d i n t o r u l e s and f a c t s and captured by some knowledge r e p r e s e n t a t i o n (KR) f o r m a l i s m . This f a l l a c y leads t o the view t h a t knowledge a c q u i s i t i o n (KA) i s a t e c h n i c a l process which t r a n s l a t e s human meanings i n t o symbols which can then be operated upon by a computer" t o r e c o n s t r u c t s p e c i f i c forms of i n t e l l i g e n t human beh a v i o r . This view was successful in r e p r o d u c i n g some forms of s p e c i f i c human behavior' and p r o v i d e d the f o u n d a t i o n for' the e a r l y success of AI research ( c f . Minsky [ 1 6 ] , Newell & Simon [ I ? ] ) . Therefore i t has deeply i n f l u e n c e d many researchers in the f i e l d . This view i s too narrow because t h e r e i s l i t t l e reason t o b e l i e v e t h a t i t w i l l e v e n t u a l l y be succ e s s f u l at b u i l d i n g 'r'eal ~ ES in the domains in which ' r e a l ' e x p e r t s work, such as law, management p l a n n i n g , f i n a n c e , systems design e t c . structur'es that
OBJECTIVE OF THE PAPER
This paper--~'nquires i n t o some of the p r i n c i p a l problems of KA. I t i d e n t i f i e s t h r e e types of i m p l i c i t background knowledge t h a t e x p e r t s r o u t i n e l y and u n r e f l e c t i n g l y use in everyday problem s o l v i n g . Although t h i s knowledge i s not w i d e l y acknowledged by c u r r e n t methods of KA, i t has been found t h a t they help e x p l a i n many of these problems [ 1 8 , 2 0 ] . Current t h e o r y in ES design recognizes only two types of knowledge: f a c t s and r u l e s , q u e s t i o n s which a r i s e are whether" t h i s i s a good d i c h o tomy and whether" i t i s s u f f i c i e n t , or' do we need t o consider' other" types of knowledge? I f more types are necessary, do we need d i f f e r e n t methods of KA, or' can we r e l y on one method for' a l l types of knowledge? Research on knowledge types i s i m p o r t a n t , they d i r e c t l y i n f l u e n c e the d e v e l o p e r ' s o p i n i o n about which knowledge needs t o be a c q u i r e d and how d i f f e r e n t types of knowledge are used in problem s o l v i n g . It also discusses epistemological issues relevant to the elicitation of k n o w l e d g e (KA) f r o m e x p e r t s , and t h e i m p o r t a n c e o f t h e s e i s s u e s t o t h e development of knowledge systems (KS). It discusses the difficulties t h a t c o n f r o n t the KE when d e a l i n g w i t h e x p e r t i s e t h a t i s h e a v i l y dependent on e x p e r i e n t i a l knowledge. T h e p a p e r i s c o n c e p t u a l l y organized i n t h r e e blocks= (a) c o n c e p t u a l , (b) t h e o r e t i c a l and (c) n o r m a t i v e . Section (a) presents some b a s i c concepts of ES and knowledge e n g i n e e r i n g Section (b) focuses on e p i s t e m o l o g i c a l i s s u e s , KA problems, and on i d e n t i f y i n g p r o b l e m a t i c knowledge types which the KE must face in p r a c t i c e . Section (c) o u t l i n e s a KA s t r a t e g y for' d e a l i n g w i t h these problems. By f o c u s i n g on these kinds of i s s u e s , the paper hopes t o reveal reasons for' the apparent b o t t l e n e c k in KA and thereby s t i m u l a t e f u r t h e r ' i n q u i r y .
67
68
Proceedings o f t h e l l t h Annual Con~rence on Computers & IndustfialEn~neefing
WHAT ARE KNOWLEDGE SYSTEMS? KS are d i f f e r e n t i a t e d from o t h e r types of e x p e r t or' knowledge worker' support systems which are based on a l g o r i t h m s or" h e u r i s t i c procedures. One working d e f i n i t i o n of ES i s p r o v i d e d by the B r i t i s h Computer' Societ y : An ES i s regarded as the embodiment w i t h i n a computer' of a knowledge-based component, from an e x p e r t s k i l l , in such a form t h a t the system can offer, i n t e l l i g e n t advice or" take an i n t e l l i g e n t d e c i s i o n about a processing f u n c t i o n . A d e s i r a b l e a d d i t i o n a l c h a r a c t e r i s t i c , which many would consider' fundamental, i s the c a p a b i l i t y of the system, on demand, t o j u s t i f y i t s own l i n e of reasoning in a manner" d i r e c t l y i n t e l l i g i b l e t o the e n q u i r e r . (quoted by Forsyth [ I 0 ] ) .
An ES i s expected t o perform a c t i o n s t h a t would n o r m a l l y be considered human; f o r example, g i v i n g advice and t a k i n g i n t e l l i g e n t d e c i s i o n s . I t i s a l s o expected t o e x p l a i n i t s l i n e of ~r'easoning t and t r a n s f e r knowledge t o a human user' when i n s t r u c t e d t o do so. The system i s expected t o act as an i n t e l l i g e n t a s s i s t a n t t o human a c t o r s . Here i s a l i s t of t y p i cal a c t i o n s which users and KE have come t o expect of any KS: I . A s s i s t s in the s o l u t i o n of a n o n - t r i v i a l problem t h a t would o t h e r w i s e be the purview of a human e x p e r t . 2. Provides i n t e l l i g e n t advice t o the user'. 3. A s s i s t s in the e l i c i t a t i o n of f a c t s p e r t i n e n t t o the problem domain. A. Takes i n t e l l i g e n t d e c i s i o n a c t i o n s when a p p r o p r i a t e . KNOWLEDGE ENGINEERING Knowledge e n g i n e e r i n g i s concerned w i t h requirements s p e c i f i c a t i o n , des i g n , and i m p l e m e n t a t i o n of knowledge-based ES. KA i s aimed a t a r r i v i n g a t an unambiguous d e s c r i p t i o n of the contents of the knowledge-based component of the KS. Buchanan [ 6 ] d e f i n e s KA as ' t h e transfer" and tr'ansf o r m a t i o n of problem s o l v i n g exper,t i s e from some knowledge source t o a (computer') pr'ogram. ~ For' the purposes of t h i s paper', KA r e f e r s t o the process by which the knowledge r e q u i r e d for" a would be ES t o f u n c t i o n e f f e c t i v e l y i s e l i c i t e d and s p e c i f i e d . The q u a l i t y of KA i s of c r i t i c a l importance for' b u i l d i n g successful KS a p p l i c a t i o n s because the v a l i d i t y of the c o n c l u s i o n s of a KS depends on the q u a l i t y of the premises on which they are based. .
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Content Analysis Concept Induction Personal Construct Theory Psychological Scaling Techniques Discourse Analysis .
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Recent approaches t o KA
KNOWLEDGE ACQUISITION AND EPISTEMOLOGY Knowledge e n g i n e e r i n g i s i n e x t r i c a b l y l i n k e d t o e p ist e mo lo g y by v i r t u e of i t s o b j e c t of i n t e r e s t [ 1 8 ] . While knowledge e n g i n e e r i n g i s concerned w i t h i d e n t i f y i n g and t r a n s l a t i n g i n t o computer" pr'ocessable form the knowledge t h a t human e x p e r t s apply in t h e i r ' work, epistemology i s concer'ned w i t h questions such as: ( I ) What i s knowable? (2) What i s pr'obable vs c e r t a i n knowledge? (3) What are the c r i t e r i a o f j u s t i f i c a t i o n for' knowledge? (4) How are knowledge and human a c t i o n r'elated? These e p i s t e m o l o g i c a l q u e s t i o n s , though seemingly a b s t r a c t , cannot be ignored by the p r a c t i c e of knowledge e n g i n e e r i n g . Fr'om a knowledge e n g i n e e r i n g standp o i n t , Question I may be r e f o r m u l a t e d as two separ'ate p r a c t i c a l quest i o n s : (a) Can a l l human knowledge be made e x p l i c i t for' computer' p r o cessing? (b) Can the KE r e p r e s e n t what the e x p e r t knows and makes public? questions 2 and 3 m o t i v a t e still more p r a c t i c a l questions: ( c ) How s h o u l d t h e KE c l a s s i f y , m o d e l , and v a l i d a t e different types of knowl e d g e ? Q u e s t i o n 4 a l s o has p r a c t i c a l implications. For' e x a m p l e , how i s k n o w l e d g e used by human e x p e r t s d u r i n g p r ' o b l e m s o l v i n g ? Moreover, is human p r o b l e m s o l v i n g limited to expressible k n o w l e d g e , or' a r e t h e r e other" factors involved a b o v e and b e y o n d w h a t can be e x p r e s s e d .
A l t h o u g h much r e s e a r c h n e e d s t o be done on t h e s e q u e s t i o n s the implications s h o u l d be c l e a r ' . The p o s i t i o n one t a k e s on t h e i s s u e s i n f l u e n c e s the fundamental characteristics of theories, k n o w l e d g e and a s s u m p t i o n s upon w h i c h t h e s e l e c t i o n / d e v e l o p m e n t o f KA m e t h o d s a r e b a s e d . I n t h i s regard, it is most disturbing to find that these questions r'emain largely unexamined in the literature on KA f o r ' e x p e r t s y s t e m s .
Ngwenyamaet
al.:
Epistemologyand knowledge acquisition
PROBLEMS OF KNOWLEDGE ACQUISITION KA in pr'a~'ice ~as proven to be complex, time consuming, and often un-
successful. Several approaches have been suggested ( o f . Table I . ) . However, each s u f f e r s some of the l i m i t a t i o n s l i s t e d below. The research community is f a r from a general theory of KA, and much t h e o r e t i c a l work needs to be done. Research and p r a c t i c e in KA have continued to uncover problems and puzzles. We b e l i e v e t h a t at the heart of the matter' l i e s in the e p i s t e m o l o g i c a l questions o u t l i n e d above. These problems are widely reported in the l i t e r a t u r e and are l i s t e d here f o r the reader: I . I n a b i l i t y of subjects to a r t i c u l a t e t h e i r ' problem s o l v i n g s k i l l s . 2. I n a b i l i t y of KEs to e l i c i t i m p l i c i t knowledge. 3. Inconsistency in s u b j e c t s ' d e s c r i p t i o n of t h e i r problem solving s t r a t e g i e s . ~. Incompleteness in the d e s c r i p t i o n s of problem s o l v i n g s t r a t e g i e s . 5. I n a b i l i t y of the KS developer to understand the s u b j e c t ' s d e s c r i p t i o n of the problem s o l ving s t r ' a t e g i e s . 6. I n a b i l i t y to understand and capture the openness of problem s o l v i n g to i n v e n t i o n and novel a p p l i c a t i o n s of knowledge. THREE TYPES OF IMPLICIT KNOWLEDGE
Ngwenyama ~ a ~ ' - i d e n t i f i e d three types of i m p l i c i t background knowledge t h a t has been c o n t r i b u t i n g to the above mentioned problems. I t is important to note t h a t experts operate with two l e v e l s of knowledge: (a) Present-at-hand, t h a t which they consciously r e f l e c t on during problem s o l v i n g ; and (b) Ready-at-hand, t h a t which remains in the backgroung, u n r e f l e c t e d upon during problem s o l v i n g . For' the moment we must focus on the types of background knowledge. Together these types of knowledge take the form of a t a k e n - f o r - g r a n t e d s u b j e c t i v e reference schema t h a t enables experts t o act and i n t e r p r e t the meaningfulness or" meaninglessness of a c t i o n s w i t h i n the work environment. Embedded in t h i s schema are: ( I ) R o u t i n e / I n s t i n c t i v e knowledge. (2) Tacit k n o w l e d g e / s k i l l s , and (3) I n t u i t i v e knowledge/ knowledge of recipes. These are e s t a b l i s h e d in work r o u t i n e s or' p r e w r ' i t t e n unquestioned s o l u t i o n s c r i p t s from past experience t h a t remain u n r e f l e c t i v e l y ready-at-hand for' the e x p e r t . In p r a c t i c e the KEs and experts e x h i b i t poor' c a p a b i l i t i e s for' s u r f a c i n g , q u e s t i o n i n g , and c a p t u r i n g these types of knowledge. Routine or I n s t i n c t i v e knowledge, presents the actor" w i t h d e f i n i t i v e ~ n s to problems which are organized in the f l o w of l i v e d exper'iences, w i t h o u t him having to be conscious of them. E s s e n t i a l l y , i t is the understanding of s o l u t i o n s to everyday work occurrences or puzzles which were once problematic but have been d e f i n i t i v e l y solved by some i n a r ' t i c u l a b l e s k i l l or" a r t . This means t h a t r o u t i n e knowledge can subordinate, c o o r d i n a t e , or" even dominate other aspects of knowledge in a c t i o n - s i t u a t i o n s w i t h o u t the a c t o r being conscious of i t . The e f f e c t i v e n e s s of t h i s type of knowledge becomes s u b j e c t i v e l y c e r t a i n to the user" through r e peated success under a p p l i c a t i o n . I t is thus t r u s t w o r t h y , unquestionably r e a l i z a b l e in every p r o b l e m - s i t u a t i o n , standardized and included in the store of t a k e n - f o r - g r a n t e d elements ready to be grasped at any time f o r s o l u t i o n s to s p e c i f i c problems. Routine or' i n s t i n c t i v e knowledge may be defined as innate knowledge such as t h a t p o s t u l a t e d by Chomsky with regards to human language competence.
Chomsky claims t h a t i n d i v i d u a l s have knowledgeof language beyond t h a t which they are able to d e s c r i b e , but which they use in everyday l i n g u i s t i c a c t i o n [ 6 ] . In support of t h i s argument, he p o i n t s to young c h i l d r e n who are able to e f f e c t i v e l y construct proper l i n g u i s t i c utterances, a l b e i t w i t h some d i f f i c u l t y , f a r " beyond t h e i r ' experience or' e x p l i c i t knowledge of the language. The primary c h a r a c t e r i s t i c of t h i s type of knowledge is t h a t i t is unexplainable, but is expressed in the form of accomplishing a r e s u l t without p r i o r experience t h a t is in any way commensurate with the accomplishment. T a c i t knowledge, is a n o n - a r t i c u l a b l e s k i l l which the expert knows he or d i s t i l l e d from p r i o r ' experience and consciously a p p l i e s in problem s o l v i n g . T a c i t knowledge is t h a t which can only be t r a n s f e r r e d by exemplars (an exemplar ~s a recorded approach t h a t has been a p p l i e d s u c c e s s f u l l y to a d i f f i c u l t and evasive problem). A good exmple is the t e a c h i n g of someone how to r i d e a b i c y c l e . I t has been noted t h a t no one i s able to e x p l a i n t o a student how to r i d e a b i c y c l e . The only pedagogical technique useful in t h i s s i t u a t i o n is f o r the teacher get onto the v e h i c l e and show the student how i t is done. ~ a s
The t h i r d type is i n t u i t i v e knowledge or knowledge o_f r e c i p e s . This type of knowledge i s in o p e r a t i o n when, being faced with a new problem s i t u a t i o n , we c o n s t r u c t an ad hoc theory to help us i n t e r p r e t the s i t -
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u a t i o n and develop s o l u t l o n s t r a t e g i e s . A good example i s ' s e a t of the p a n t s ' management d e c i s i o n making, where the manager i s not i n t e r e s t e d in d e v e l o p i n g general t h e o r i e s or" any s c i e n t i f i c t e s t i n g as i s proposed by s c i e n t i f i c management t h e o r i e s . I n s t e a d , he or' she has a l a r g e 'bag of t r i c k s ' and wishes t o c o n s t r u c t an e x p l a n a t i o n of the pr'oblem s i t u a t i o n b e f o r e t a k i n g a c t i o n . Often the manager' considers the 'bag of t r ' i c k s ~ t o be v a l i d a p r i o r i , confirmed by on the job e x p e r i e n c e . I n t e r e s t is confined m ~ i n - - ~ scanning the problem space f o r c e r t a i n recognizable patterns to confirm the manager"s 'gut feeling' The power' o f i n t u i t i v e k n o w l e d g e can be seen a t w o r k i n e v e r y t y p e o f problem solving activity, from simple everyday problems to the construction of scientific h y p o t h e s e s for" t e s t i n g , Ideally this type of knowledge encompasses a corpus of well tried and a c c e p t e d c o n c e p t s ( b a g of tricks) of the experienced problem solver', Moreover, it is likely to be u n s t r u c t u r e d and h i g h l y selective, unless reflected upon i n a c o n s c i o u s m a n n e r by t h e i n d i v i d u a l . A KA STRATEGY
FOR D I F F I C U L T
INDUSTRIAL
APPLICATIONS
T n ~ s t r i a l wor'~--~ractices w h ~ e x p T i c i t definition in engineering procedure manuals, and require years of practical experience to master, can be expected to cause significant problems to KA. This is because the expert's knowledge of such procedures is usually r'ooted in vast diffused experience of working with particular operations. In 'normal working conditions' the expert knows what to do without having to reflect or" articulate the decision processes. Established work routines, pr'ewritten and unquestioned scripts provide ready-at-hand knowledge which enable the expert to perform smoothly without conscious awareness. However', difficult novel problem s i t u a t i o n s and f a i l u r e s f o r c e the e x p e r t t o r e f l e c t on h i s / h e r work r o u t i n e s [ 1 9 ] . This i s because such problems i t u a t i o n s t h r e a t e n the ' t r i e d and accepted' s o l u t i o n s t r a t e g i e s . They a l s o c o n t r i b u t e t o an i m p o r t a n t c o n d i t i o n , a ' c o g n i t i v e opening' which f o r c e s t h e e x p e r t t o s u r f a c e and s c r u t i n i z e his/her' ready-at-hand routines to ascertain w h e r e t h e y have b r o k e n d o w n , T h i s i n s i g h t has l e a d t o continuing r e s e a r c h and d e v e l o p m e n t on specific KA s t r a t e g i e s , one o f which is described b e l o w ( s e e a l s o Ngwenyama e t a l . [19]). A FOUR STAGE KNOWLEDGE A C Q U I S I T I O N STRATEGY One "&'t'ra-t-egy t K a t we have found e f f e c t i v e f o r d e a l i n g w i t h the problems of c a p t u r i n g embedded i m p l i c i t knowledge i s d e f i n e d in the f o l l o w i n g f o u r step procedure conducted w i t h two KEs and the exper't. The process i s a hermeneutic c y c l e in which the knowledge engineers must f i r s t (Stage I) o r i e n t themselves t o the a p p l i c a t i o n area. In Stage 2, the KEs enter" a d i a l o g u e w i t h the exper't. In Stage 3 case s t u d i e s of v a r i o u s l e v e l s of d i f f i c u l t y are pr'esented t o the e x p e r t for' s o l u t i o n , which the KEs w i l l observe, d i s c u s s , and analyze w i t h the help of the e x p e r t . F i n a l l y in Stage a, the h e u r i s t i c s are compiled and normalized for" coding and t e s t i n g w i t h the ES softwar'e. .
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A Four' Stage Model For" Knowledge A c q u i s i t i o n .
In Stage I the KEs should a t t e m p t t o become conversant w i t h the a p p l i c a t i o n area. Ngwenyama e t a l . [19] found t h i s t o be a c r u c i a l stage t o the success of the KA process. The f u t u r e communication and mutual unders t a n d i n g which must be f o r g e d between the e x p e r t ( s ) and KEs, hinges on how w e l l the KEs are s o c i a l i z e d i n t o the e x p e r t ( s ) ' work environment. The i n t e n t here i s not t o make the KEs e x p e r t s in the a p p l i c a t i o n area
Ngwenyama et
al.:
Epistemology and knowledge acquisition
but to provide them with the necessary base knowledge and professional language to communicate e f f e c t i v e l y [B]. Although the communication aspect has been over' looked in KA research, i t is important to remember t h a t knowledge engineering l i k e systems analysis is a social process. Rolandi supports t h i s notion; he argues t h a t the most c r i t i c a l s k i l l s of the KE are s o c i a l . Two e f f e c t i v e s t r a t e g i e s of o r i e n t a t i o n are s e l f study and p a r t i c i p a n t observation. KEs should study procedure manuals, engineering handbooks, appropriate textbooks, and reference material p e r t i n e n t to the f i e l d . P a r t i c i p a n t observation could take the form of short term apprenticeship, working along with the expert, etc. This aspect should lead to the development of mutual respect and a good rapport among the p a r t i e s , the b u i l d i n g blocks of e f f e c t i v e communication. In the d i a l o g stage the KEs conduct two types of interviews with the expert: (a) unstructured (b) structured. The unstructured i n t e r v i e w is the beginning of the e l i c i t a t i o n process and should be extensive and protracted. I t is from these interviews t h a t the KEs w i l l obtain most of present-at-hand knowledge and i n s i g h t s f o r constructing the structured i n t e r v i e w s . A large number' of the unstructured interviews need to be undertaken while the expert is performing f a m i l i a r tasks, a l l o w i n g f o r more spontaneous questioning. Structured interviews should examine s p e c i f i c problem solving routines and concepts that need c l a r i f i c a t i o n . The information gathered at the previous stages is used to construct cases of varying degrees of d i f f i c u l t y . These are then used in c o n t r ' o l l ed s i t u a t i o n s where the expert is observed throughout the problem solving process. The case technique is widely supported in the l i t e r a t u r e . Buchanan et a l . [5] have strongly advocated the use of cases as an approach to capturing the refined and subtle aspects of the e x p e r t ( s ) ' reasoning. There are two approaches to these cases that we have found successful in helping the team to surface ready-at-hand knowledge: (a) control of problem complexity, and (b) control of wor'king i n f o r m a t i o n . In the f i r s t approach, cases of varying l e v e l s of problem complexity are presented to the expert f o r s o l u t i o n . In the second approach the expert is asked to solve s p e c i f i c cases with gradually increasing l e v e l s of p e r t i n e n t information. One of the primary goals of t h i s second case approach should be to help the expert to 'breakdown' and surface takenf o r - g r a n t e d work routines. Well defined challenging case problems of various l e v e l s of d i f f i c u l t y have been found e f f e c t i v e vehicles to stimu l a t e the expert through var'~ous l e v e l s of r e f l e c t i o n on his/her work routines [19]. This approach when conceived with appropriate problems could help the KE with the e l i c i t a t i o n of t a c i t and i n t u i t i v e knowledge. The g o a l o f Stage 4 i s t o c l e a n up t h e i n f o r m a t i o n collected in the previous stages of the KA process. The main o b j e c t i v e is to i d e n t i f y and c l a r i f y and remove d u p l i c a t e ~eur'istics. I t should be conducted with the assistance of the expert. DOCUMENTATION TECHNIQUES AND TOOLS During the dialogue and case ~ s i s stages, large volumes of infor'mat i o n are produced which must be c a r e f u l l y documented f o r analysis. We found that note taking is not the most e f f e c t i v e form of documentation. Although the s i t u a t i o n improves with two KEs, i t breaks down under' the burden of the i n f o r m a t i o n . Many r;esearchers have advocated the recording of p r o t o c o l s ; we have found them i n d i s p e n s i b l e . Graphical techniques a r e also important f o r the analysis and normalization of the h e u r i s t i c s . Decision network diagrams supported by decision tables are useful, clear', concise, and are widely accepted for" i n d u s t r i a l a p p l i c a t i o n s . Although other forms of charting techniques might be superior, the most important issue should be e f f e c t i v e communication between the expert and KEs. The expert should have a f u l l reading knowledge of the charting techniques. This speeds up and enhances the quality of the analysis, and t h e r e s u l t i n g system. CONCLUSION This paper' o u t l i n e d several problems of KA which KEs encounter when d e veloping knowledge-based systems for' i n d u s t r i a l a p p l i c a t i o n s . These problems a r i s e from the c o n c e p t u a l i z a t i o n of the problem domain in which the ES is to operate and from the nature of the knowledge types which a r e a v a i l a b l e to human experts. These problems must e x p l i c i t l y or" i m p l i c i t l y be addressed by any approach to KS development. Whatever' the p o s i t i o n or' compromise adopted, i t w i l l be apparent from analyzing the design choices made, and the e f f i c t i v e n e s s of the d e l i v e r e d system.
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The a p p r o a c h t a k e n was t o r e l a t e t h e c u r r e n t v i e w s on t h e p r o b l e m s o f KA t o t h e k n o w l e d g e t y p e s on w h i c h human e x p e r ' t s r ' e l y when t h e y i n t e r p r e t p r o b l e m s , seek o u t r e l e v a n t i d e a s , p r o c e s s e v i d e n c e e t c . Such an a n a l y s i s s h o u l d be o f i n t e r e s t t o ES d e v e l o p e r s b e c a u s e i t h e l p s them t o understand the limitations i n t r o d u c e d by t h e a d o p t i o n o f a s i m p l e r ' u l e f a c t t h e o r y o f k n o w l e d g e . In f a c t , t h e KA b o t t l e n e c k e x i s t s because t h e r e a r e as y e t no a p p r o a c h e s w h i c h f o r m a l i z e many o f t h e k n o w l e d g e t y p e s we d i s c u s s e d . N e v e r t h e l e s s , progress in overcoming the limitations o f c u r r e n t ES d e v e l o p m e n t a p p r o a c h e s w i l l be f a c i l i t a t e d i f we a r e b e t t e r ' i n f o r m e d a b o u t why t h e p r o b l e m o f KA i s so c o m p l e x and what causes the difficulties [ 1 2 ] . Based on our' c u r r ' e n t r e s e a r c h , we have been a b l e t o d e v i s e some a p p r o a c h e s t o g e t a r o u n d t h e d i f f i c u l t T e s (one o f w h i c h we r e p o r t e d i n p a r t h e r e ) . R e s e a r c h on d e v e l o p i n g a g e n e r a l t h e o r y o f k n o w l e d g e t y p e s and m e t h o d s f o r ' KA i s c o n t i n u i n g , and has been reported in [11,19]. REFERENCES [1] B a r ' r , A . , and E.A. F e i g e n b a u m , "The Handbook o f A I " , V o l . 2,Mor'gan Kaufmann, Los A l t o s , C a l i f o r n i a , 1981.
[2]
Belkin N. d. et a l . , " E l i c i t a t i o n using discourse a n a l y s i s " , in Knowledge A c q u i s i t i o n for' Knowledge-Based Systems Workshop, Banff, Canada, Nov. 1986.
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o f O r g a n i c C h e m i s t r y " , Machine I n t e l l i g e n c e M i c h i e , D . , 1970, p p . 209 - 25&. B u c h a n a n , B. G . , and R.O. Duda, " P r i n c i p l e s S y s t e m s " , A d v a n c e s i n C o m p u t e r s , V o l . 22, N. Chomsky, N . , " K o n w ~ d g e o f L a n g u a g e " , i n K. Minnesota Studies in Philosophy of Science, o f M i n n e s o t a P r e s s , M i n e s s o t a , 1975. C h o r a f a s , D. N. " A p p l y i n g E x p e r t Systems i n
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