Chapter 15 Inferences, Explanaions and Uncertainty15章的推论解释与不确定性

1、1chapter 15: inferences, explanations and uncertainty 15.1 opening vignette: konica automates a help desk with case-based reasoningthe problemnkonica business machines wanted to fully automate its help desk for internal support external supportdecision support systems and intelligent systems, efraim

2、 turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj2the solutionnmost promising approach case-based reasoningnsoftware artistrys expert advisor could run multiple problem resolution modes decision trees adaptive learning tech search more used standard cases initially late

3、r used real cases to boost accuracy includes digital photosdecision support systems and intelligent systems, efraim turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj3expert advisornused by internal tech support (6750 people)nused by customersntech support handles unusual

4、 casesnearly stage testing: 65% hit ratenadaptive learning being addeddecision support systems and intelligent systems, efraim turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj415.2 reasoning in artificial intelligencenonce knowledge is acquired, it must be stored and pr

5、ocessed (reasoned with)nneed a computer program to access knowledge for making inferences nthis program is an algorithm that controls a reasoning processninference engine or control programnrule interpreter (in rule-based systems)nthe inference engine directs the search through the knowledge basedec

6、ision support systems and intelligent systems, efraim turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj5how people reason andsolve problemssources of powernformal methods (logical deduction)nheuristic reasoning (if-then rules)nfocus-common sense related toward more or le

7、ss specific goalsndivide and conquernparallelismdecision support systems and intelligent systems, efraim turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj6nrepresentationnanalogynsynergynserendipity (luck)(lenat 1982)sources of power translated to specific reasoning or i

8、nference methods (table 15.1)decision support systems and intelligent systems, efraim turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj7t ta ab bl le e 1 15 5. .1 1 r r e ea as so on ni in ng g m m e et th ho od ds sm m e et th ho od dd d e es sc cr ri ip pt ti io on nd

9、d e ed du uc ct ti iv ve er re ea as so on ni in ng gm m o ov ve e f fr ro om m a a g ge en ne er ra al l p pr ri in nc ci ip pl le e t to o a a s sp pe ec ci if fi ic ci in nf fe er re en nc ce e. .g g e en ne er ra al l p pr ri in nc ci ip pl le e i is s c co om m p po os se ed d o of f t tw w o o

10、 o or r m m o or re ep pr re em m i is se es s. .i in nd du uc ct ti iv ve er re ea as so on ni in ng gm m o ov ve e f fr ro om m s so om m e e e es st ta ab bl li is sh he ed d f fa ac ct ts s t to o d dr ra aw wg ge en ne er ra al l c co on nc cl lu us si io on ns s. .a a n na al lo og gi ic ca al

11、 lr re ea as so on ni in ng gd d e er ri iv ve e a an ns sw w e er r t to o a a q qu ue es st ti io on n b by y k kn no ow w n n a an na al lo og gy y. . i it ti is s a a v ve er rb ba al li iz za at ti io on n o of f i in nt te er rn na al li iz ze ed d l le ea ar rn ni in ng g p pr ro oc ce es ss

12、s( (t tu ut th hi il ll l 1 19 99 90 0 a an nd d o o w w e en n 1 19 99 90 0 ) ). . u u s se e o of f s si im m i il la ar r, ,p pa as st t e ex xp pe er ri ie en nc ce es s. .f fo or rm m a al l r re ea as so on ni in ng gs s y yn nt ta ac ct ti ic c m m a an ni ip pu ul la at ti io on n o of f d d

13、a at ta a s st tr ru uc ct tu ur re e t to od de ed du uc ce e n ne ew w f fa ac ct ts s, , f fo ol ll lo ow w i in ng g p pr re es sc cr ri ib be ed d r ru ul le es s o of fi in nf fe er re en nc ce es s ( (e e. .g g. ., , p pr re ed di ic ca at te e c ca al lc cu ul lu us s) ). .p p r ro oc ce ed

14、du ur ra al l( (n nu um m e er ri ic c) )r re ea as so on ni in ng gu u s se e o of f m m a at th he em m a at ti ic ca al l m m o od de el ls s o or r s si im m u ul la at ti io on n ( (e e. .g g. ., ,m m o od de el l- -b ba as se ed d r re ea as so on ni in ng g, , q qu ua al li it ta at ti iv ve

15、e r re ea as so on ni in ng ga an nd d t te em m p po or ra al l r re ea as so on ni in ng g- - -t th he e a ab bi il li it ty y t to o r re ea as so on na ab bo ou ut t t th he e t ti im m e e r re el la at ti io on ns sh hi ip ps s b be et tw w e ee en n e ev ve en nt ts s) ). .m m e et ta al le e

16、v ve el lr re ea as so on ni in ng gk k n no ow w l le ed dg ge e a ab bo ou ut t w w h ha at t y yo ou u k kn no ow w ( (e e. .g g. ., , a ab bo ou ut t t th he ei im m p po or rt ta an nc ce e a an nd d r re el le ev va an nc ce e o of f c ce er rt ta ai in n f fa ac ct ts s a an nd d/ /o or rr ru

17、 ul le es s) ). .decision support systems and intelligent systems, efraim turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj8reasoning with logic modus ponensif a, then b a and (a b) b a and (a b) are propositions in a knowledge basemodus tollens: when b is known to be fa

18、lseresolution: combines substitution, modus ponens and other logical syllogismsdecision support systems and intelligent systems, efraim turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj915.3 inferencing with rules: forward and backward chaining firing a rule: when all of

19、 the rules hypotheses (the “if parts”) are satisfiedcan check every rule in the knowledge base in a forward or backward directioncontinues until no more rules can fire, or until a goal is achieveddecision support systems and intelligent systems, efraim turban and jay e. aronsoncopyright 1998, prenti

20、ce hall, upper saddle river, nj10forward and backward chainingchaining: linking a set of pertinent rulessearch process: directed by a rule interpreter approach: forward chaining. if the premise clauses match the situation, then the process attempts to assert the conclusion backward chaining. if the

21、current goal is to determine the correct conclusion, then the process attempts to determine whether the premise clauses (facts) match the situationdecision support systems and intelligent systems, efraim turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj11backward chainin

22、g ngoal-driven - start from a potential conclusion (hypothesis), then seek evidence that supports (or contradicts) itnoften involves formulating and testing intermediate hypotheses (or subhypotheses)decision support systems and intelligent systems, efraim turban and jay e. aronsoncopyright 1998, pre

23、ntice hall, upper saddle river, nj12forward chaining ndata-driven - start from available information as it becomes available, then try to draw conclusionsnwhat to use?if all facts available up front (as in auditing) - forward chainingdiagnostic problems - backward chainingdecision support systems an

24、d intelligent systems, efraim turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj13a is a is in focus 15.1: the functi ons of the inference engi nein focus 15.1: the functi ons of the inference engi ne1.1. fi re the rul esfi re the rul es2.2. present the user w i th questi

25、 onspresent the user w i th questi ons3.3. a dd the answ er to the es bl ackboard (asserti on base)a dd the answ er to the es bl ackboard (asserti on base)4.4. infer a new fact from a rul einfer a new fact from a rul e5.5. a dd the i nference fact to the bl ackboarda dd the i nference fact to the bl

26、 ackboard6.6. m atch the bl ackboard to the rul esm atch the bl ackboard to the rul es7.7. if there are any m atches, fi re rul esif there are any m atches, fi re rul es8.8. if there are tw o further m atches, check to see i f goal i s reachedif there are tw o further m atches, check to see i f goal

27、 i s reached9.9. fi re the l ow est num bered unfi red rul efi re the l ow est num bered unfi red rul edecision support systems and intelligent systems, efraim turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj14the program w orks through the know l edge base unti l i tth

28、e program w orks through the know l edge base unti l i tcan post a fact (or a parti al fact i f certai nty factors arecan post a fact (or a parti al fact i f certai nty factors arebei ng used) to the bl ackboard.bei ng used) to the bl ackboard.o nce a fact has been posted, the system goes back too n

29、ce a fact has been posted, the system goes back tothe know l edge base to i nfer m ore facts. thi s conti nuesthe know l edge base to i nfer m ore facts. thi s conti nuesunti l the present goal i s achi eved or unti l al l rul es haveunti l the present goal i s achi eved or unti l al l rul es havebe

30、en fi red.been fi red.decision support systems and intelligent systems, efraim turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj1515.4 the inference tree (goal tree or logical tree)nschematic view of the inference processnsimilar to a decision tree (figure 15.2)ninferenc

31、ing: tree traversalnadvantage: guide for the why and how explanationsdecision support systems and intelligent systems, efraim turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj1615.5 inferencing with frames nmuch more complicated than reasoning with rulesnslot provides fo

32、r expectation-driven processingnempty slots can be filled with data that confirm expectationsnlook for confirmation of expectationsnoften involves filling in slot valuesncan use rules in frames nhierarchical reasoning decision support systems and intelligent systems, efraim turban and jay e. aronson

33、copyright 1998, prentice hall, upper saddle river, nj1715.6 model-based reasoning nbased on knowledge of structure and behavior of the devices the system is designed to understandnespecially useful in diagnosing difficult equipment problemsncan overcome some of the difficulties of rule-based es (ais

34、 in action 15.2)nsystems include a (deep-knowledge) model of the device to be diagnosed that is then used to identify the cause(s) of the equipments failurenreasons from first principles (common sense)noften combined with other representation and inferencing methods.decision support systems and inte

35、lligent systems, efraim turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj18a a i is s i in n a a c ct ti io o n n 1 15 5. .2 2: : m m o o d d e el l- -b b a as se ed d e e s s h h e el lp p s s t th h e ee e n n v vi ir ro o n n m m e en n t t w w e es st ti in n g g h h

36、 o o u u s se e s s a av va an n n n a ah h r r i iv ve er r c c o o m m p p a an n y y p p r ro o j je ec ct t t to o d d e ev ve el lo o p p a a r re ep p r re es se en n t ta at ti io o n n s sc ch h e em m a a f fo o r re en n g g i in n e ee er ri in n g g a an n d d c co o m m m m o o n n - -s

37、 se en n s se e k kn n o o w w l le ed d g g e e a ab b o o u u t te en n v vi ir ro o n n m m e en n t ta al l a an n d d b b i io o l lo o g g i ic ca al l i im m p p a ac ct ts s o o f f a a n n u u c cl le ea ar rw w e ea ap p o o n n s s p p r ro o c ce es ss si in n g g f fa ac ci il li it ty

38、y o o p p e er ra at ti io o n n s s l l e ea ar rn n b b y y d d o o i in n g g a ap p p p r ro o a ac ch h c c y yc c: : a a s se et t o o f f g g e en n e er ra al l k kn n o o w w l le ed d g g e e a ab b o o u u t t t th h e e w w o o r rl ld d n n e ew w c co o n n c ce ep p t ts s a ar re e a

39、 ad d d d e ed d t to o t th h e e q q u u a al li it ta at ti iv ve e m m o o d d e el l d d y yn n a am m i ic c e ev ve en n t ts s c ca an n b b e e d d e es sc cr ri ib b e ed d r r e el le ev va an n t t k kn n o o w w l le ed d g g e e p p o o r rt ti io o n n s s m m a ay y b b e e s sh h a

40、ar re ed d o o r rr re eu u s se ed d m m o o d d e el l- -b b a as se ed d e e s s c ca an n o o v ve er rc co o m m e e s so o m m e e d d i if ff fi ic cu u l lt ti ie es s o o f fr ru u l le e- -b b a as se ed d e e s sdecision support systems and intelligent systems, efraim turban and jay e. ar

41、onsoncopyright 1998, prentice hall, upper saddle river, nj19nmodel-based es tend to be transportable”nsimulates the structure and function of the machinery being diagnosednmodels can be either mathematical or component nnecessary condition is the creation of a complete and accurate model of the syst

42、em under studynespecially useful in real-time systemsdecision support systems and intelligent systems, efraim turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj2015.7 case-based reasoning (cbr)nadapt solutions used to solve old problems for new problemsnvariation - rule-i

43、nduction method (chap. 13)nbut, cbrfinds cases that solved problems similar to the current one, andadapts the previous solution or solutions to fit the current problem, while considering any difference between the two situationsdecision support systems and intelligent systems, efraim turban and jay

44、e. aronsoncopyright 1998, prentice hall, upper saddle river, nj21finding relevant cases involvescharacterizing the input problem, by assigning appropriate features to itretrieving the cases with those featurespicking the case(s) that best match the input bestextremely effective in complex casesjusti

45、fication - human thinking does not use logic (or reasoning from first principle)process the right information retrieved at the right timecentral problem - identification of pertinent information whenever needed - use scriptsdecision support systems and intelligent systems, efraim turban and jay e. a

46、ronsoncopyright 1998, prentice hall, upper saddle river, nj22what is a case?ncase - defines a problem in natural language descriptions and answers to questions, and associates with each situation a proper business actionnscripts - describe a well-known sequence of events often “reasoning is applying

47、 scripts” more scripts, less (real) thinking can be constructed from historical cases case-based reasoning is the essence of how people reason from experience cbr - a more psychologically plausible expert reasoning model than a rule-based model (table 15.2) nadvantages of cbr (table 15.3) decision s

48、upport systems and intelligent systems, efraim turban and jay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj23ta ble 15.2 c om pari son of c ase-based and r ul e-ta ble 15.2 c om pari son of c ase-based and r ul e-based r easoni ngbased r easoni ngc ri teri onc ri teri onr ul e-base

49、d r easoni ngr ul e-based r easoni ngc ase-basedc ase-basedr easoni ngr easoni ngk now l edge uni tk now l edge uni tr ul er ul ec asec aseg ranul ari tyg ranul ari tyfi nefi nec oarsec oarsek now l edgek now l edgeacqui si ti on uni tsacqui si ti on uni tsr ul es, hi erarchi esr ul es, hi erarchi e

50、sc ases, hi erarchi esc ases, hi erarchi esexpl anati onexpl anati onm echani smm echani smbacktrack of rul e fi ri ngsbacktrack of rul e fi ri ngsprecedent casesprecedent casesc haracteri sti cc haracteri sti coutputoutputa nsw er, pl us confi dencea nsw er, pl us confi dencem easurem easurea nsw e

51、r, pl us precedenta nsw er, pl us precedentcasescasesk now l edge transferk now l edge transferacross probl em sacross probl em sh i gh, i f backtracki ngh i gh, i f backtracki nglow , i f determ i ni sti clow , i f determ i ni sti clowlowspeed as a functi onspeed as a functi onof know l edge baseof

52、 know l edge basesi zesi zeexponenti al , i f backtracki ngexponenti al , i f backtracki ngli near, i f determ i ni sti cli near, i f determ i ni sti clogari thm i c, i f i ndexlogari thm i c, i f i ndextree bal ancedtree bal anceddecision support systems and intelligent systems, efraim turban and j

53、ay e. aronsoncopyright 1998, prentice hall, upper saddle river, nj24c c r ri i t te er ri i o on nr r u ul l e e- -b ba as se ed d r r e ea as so on ni i n ng gc c a as se e- -b ba as se ed d r r e ea as so on ni i n ng gd d o om m a ai i n nr re eq qu ui i r re em m e en nt ts sd d o om m a ai i n

54、n v vo oc ca ab bu ul l a ar ry yg g o oo od d s se et t o of f i i n nf fe er re en nc ce e r ru ul l e es se ei i t th he er r f fe ew w r ru ul l e es s o or r r ru ul l e es s a ap pp pl l y ys se eq qu ue en nt ti i a al l l l y yd d o om m a ai i n n m m o os st tl l y y o ob be ey ys s r ru u

55、l l e es sd d o om m a ai i n n v vo oc ca ab bu ul l a ar ry yd d a at ta ab ba as se e o of f e ex xa am m p pl l e e c ca as se es ss st ta ab bi i l l i i t ty y- - -a a m m o od di i f fi i e ed d g go oo od d s so ol l u ut ti i o on ni i s s p pr ro ob ba ab bl l y y s st ti i l l l l g go oo

56、 od dm m a an ny y e ex xc ce ep pt ti i o on ns s t to o r ru ul l e es sa a d dv va an nt ta ag ge es sf fl l e ex xi i b bl l e e u us se e o of f k kn no ow w l l e ed dg ge ep po ot te en nt ti i a al l l l y y o op pt ti i m m a al l a an ns sw w e er rs sr r a ap pi i d d r re es sp po on ns

57、se er r a ap pi i d d k kn no ow w l l e ed dg ge e a ac cq qu ui i s si i t ti i o on ne ex xp pl l a an na at ti i o on n b by y e ex xa am m p pl l e es sd d i i s sa ad dv va an nt ta ag ge es sc c o om m p pu ut ta at ti i o on na al l l l y y e ex xp pe en ns si i v ve el lo on ng g d de ev ve

58、 el l o op pm m e en nt t t ti i m m e eb bl l a ac ck k- -b bo ox x a an ns sw w e er rs ss su ub bo op pt ti i m m a al l s so ol l u ut ti i o on ns sr r e ed du un nd da an nt t k kn no ow w l l e ed dg ge e b ba as se es so ou ur rc ce e: : c c o ou ur rt te es sy y o of f m m a ar rc c g g o o

59、o od dm m a an n, , c c o og gn ni i t ti i v ve e s sy ys st te em m s s, , i in nc c. . b ba as se ed d o on n: : m m . . g g o oo od dm m a an n, , p pr r i is sm m : : a a c c a as se e- -b ba as se ed dt te el l e ex x c c l l a as ss si i f fi i e er r, , i i n n a a . . r r a ap pp pa ap po o

60、r rt t a an nd d r r . . s sm m i i t th h ( (e ed ds s. .) ), , i in nn no ov va at t i i v ve e a ap pp pl l i i c ca at t i i o on ns s o of f a ar rt t i i f fi i c ci i a al l i in nt t e el l l l i i g ge en nc ce e. . v v o ol l . . 1 11 1, ,c c a am m b br ri i d dg ge e, , m m a a : : m m i