From: AAAI-86 Proceedings. Copyright ©1986, AAAI (www.aaai.org). All rights reserved. MULTIPLE FuAULTS Johan de Klcer Intelligent Systems Laboratory XEROX Palo Alto R.esenrch Center 3333 Palo Coyote Alto, Hill Road California 94304 and Brian M.I.T. C. Artilicial Intelligence 545 Cambridge, between require of an determining artifact nnd differences between and th,e predicted the manifested behuvior of the for between the diflerences diagnostic inferring edge procedure the behavior oj the nents comprising This the and the research artifact and eficient are oj diagnostic Finally, und behavior procedure) The (GDE novel due its The Sec- and manipulated in assumptions, resulting reflecting Third, the the iterative of a clear separation is drawn between diprediction, resulting in a domain (and independent diagnostic procedure. Introduction Engineers stand the and lllodels. electrical circuits to sively refine a model process of theory sense reasoning tasks models and reality. 132 / SCIENCE constantly between troubleshoot find broken based on formation. involve strive physical systems mechanical parts. empirical Many finding under- and their syst,cms or Scientists succesdata during the everyday the to differcuce commonbetween If the the is presumed assigning on observed task is differences model. Usually circuits, actual does Thus, The first, model-artifact troubleshooting determine why under and modeling to strategy circuits, a correctly to until physical diagnosis employed the diagnostic designed piece is at variance is also to derive task is to of cqltipmcnt is as it was intended; the explanation being that the particular piece consideration tests general, encompassing and analog and digi- approach of the inference from observations. the mentioned diifer- differences. is very devices Our If re- evidence difference. programs, systems. independent predictions For the of diagnosis mechanical debugging and indicate the model-artifact reflect trou- to be correct indicate part malfunctions. then the artifact is presumed requires two phases. the set of possible accurately ment scientists differences Engineers observed. model of based The second proposes evidence-gathering the set of possible model-artifact differences not functioning faulty behavior 1. a means model all model-artifact a unique This view troubleshooting tal of the differences formation, in or biological diagnostic nature admit ences. reline they First, faults. the and changes diagnostic task above, identifies General contributions: t,hen to be correct not requires parts discrepancies quired compo- to multiple represented violated model. to all model-artifact the task is theory and tested on exdigital circuits. procedure. is incremental, itself bleshooting, individual system several differences paper is model-based, device from knourl- of the The failures ond, j&lure candidates terms of minimal sets diagnosis. agnosis injerence artifact reasoning or blame behavioral behavior of the artifact model guide the setsrch function makes diagnoses procedure the in this composite device. the Engine) has been implemented the domain of troubleshooting system in an the presented of the structure Dirtgnostic amples in 02139 Diagnostic credit tasks a model Laboratory Technology Square Massachusetts, ABSTRACT Diagnostic Williams in some for the of equipway with its design (e.g., a set of components is not rectly or a set of connections is broken). To working cortroubleshoot a system, be proposed, a sequence executed and ance, or fault. consisting adders, A, U = 3, and then For of three of measurements analyzed example, multipliers, must to localize this point of variconsider the circuit in Fig. 1, Ml, Mz, and Ma, and two and A,. The inputs are A = 3, D -x 2, C =-I 2, E I= 3, and the outputs are measured showing that F 10 and = is possible of components didate and [AJZ, the MS]. 12.’ that useful that can singleton [A2,M2], differentiate to is the two highly faults. nents, addition, probable one I I Al - M2 G A2 each only - and on diagnosis and failures produced Wh en one entertains concentrated faults, tially the with work specifically method for simultaneous The if none paper which, component using paper to describes when an due is the guide to efficient the with a powerful with multiple the approach faults. in the propagation as the the fault causes the use measurement framework diagnostic for procedure inference We and domain presume (as The ture model of the obey Ihe of the device constituent simple forth, Model-artifact we behavioral without predictions being concerued presume artifact in terms obeys certain describes of its behavior I This Systclns. Law, of cirruit consists Xirchoff’s <and so on. In the is ;dso artifact differs 1rwt1 by b3t.h physical constituents. behavioral electrical circuit where wires obey Ohm’s the Each rules. of wires, Current tliaguosis, frown [z] nntl For type of csample, a generally, inference its tions [8] in cxpl:.il!ir!;; Qualitative It is In “J’ C (i.e., the is observed a symptom proccdurc, (inl’erred of these from its An impor- from is that - in terms of = short circuits; a common- as persistence, defaults case) that be inferred section for this circuit. makes assumptions details. between and E observa- the = procedure), a pre- an Given D = 3, and a genfor the which difference example from present but procedure procedure inference modelInstead, indirectly 8 we purpose, observations and about the procedure’s is any the observable.2 inference 2, any in the inference from our 2, of directly A x C + R x D = 12. However, Thus <are manifested, hypothesis; must an the Consider calculation resistors and so Law, resistors it is given that model. by A = 3, B = struc- A conmodel its Symptoms not a symptom made a which characterizing apply. absence such violations moment tion. produces differences is usually for Differences the are Intuitively, 2. models needed. of all assumption diction on longer a faulty Detection differences es- is very general. For example, in elecmight be the correct functioning behavioral observations. eral inference architecture engine. no model-artifact di- also demonelectronics, and differences <are not about component artifact itlference In addition it domain of digital models 3. pro- (2) assumptions hold. If any then the artifact deviates may of which model-artifact is based associated model In a set is a set of differences sense domain an assumption or Occam’s Razor. of of it as assumption violations. to behave according to viol&ons assumptions of analyzing a predictive predictive of inference. with of possible of which A compo- differences a set phys- of this approach ([1,2:3,6,8,11]) specify correct models for constituents in a scientific gen- number in a logical associated approach of its thus, of each exponenThis any process a general coupled provides dealing strates grows consideration. developing failures information This agnosis the including a set of measurements, constituent’s are false, assumption tronics the faulty comof multiple of measurements to identify potential discussion on (see [3] f or an extensive variance probabilistic cess). at diagnosing faults. of this focus results a single possibility space of potential candidates the number of faults under is aimed eral by steps (3) (4) Reasoning primarily concept, model-artifact diagnostic explicit circuit. has diagnosing ponent. the 1: A familiar in these is a description of its each of its constituents. has constituent, model, Fig. what grain size of the diagnosis. takes (1) the physical structure, tant ramification WC need only work even constituent possible i.e., all the assumptions Earlier to determine general model-artifact differences stituent is guaranteed -I’ D 1 or more Our C 2- a very set of candidates, each explains the observations. F B 2 the artifact models for each differences Ml for plus is tablishes the Diagnosis for diagnostician processes measure- [Ml]. of the are. constituent 1 3 task The model structure, ical produce the only the differences as a canor isolating it and r A is likely between [Al] candidates: [M,], then sets to X because it following [A,], in further is optimal measurements of the is referred mea>uring information X these one (each set by [...I): Furthermore, most From at least is faulty is designated Intuitively, ment G = to deduce inputs, 3, by simple F = X x 1’ = F is measured to be 10. to be 10, not 12” .C u,ny inconsistency 1. is a symptom. detected More by the and ber.wecn prcdic- distinct way occur oleasureiuents) two as well as n thei! Reasoning and Diagnosis: AUTOMATED REASONING / 133 measurement and a prediction (inferred from some other 5. measurements). Candidates A cnndidate 4. The Each Conflicts diagnostic procedure symptom that tual artifact of diagnosis are tells possibly faulty). us violated Intuitively, all (e.g., “F is a set with symptoms. A candidate assumptions (indicated the more components correctly. is observed lation that E’ = Ml, M2 and Al, functioning, then by or consistent that nmy to Consider be be of assumptions sup- and thus leads to an inconsistency. might be a set of components be functioning sylllptonl one a conflict porting a symptom, electronics, a conflict cannot is guided about 10, not our 12.” In which Our perset of (Ml, set Mz, value E = E = rise on MS that correctly first (Al, Al, To be far. by a set assumption As every (i.e., its have a non-empty of assumptions mentioned candidate conflicts), each in must set the explain representing intersection a with every of teed electronics, where (any to be a candidate components working. Before taken we know nothing initial candidate space any about measurements the grows ber of components. Any faulty, thus the candidate of 2” = 32 candidates. is a set of failed mentioned a.re not circuit. The exponentially component space for compoguaran- have been size of the with the num- could be working or Fig. 1 initially consists suno all [MI.MZ.MJ.Al) reduce a conflict. if it has This observation of our diagnostic tion is to identify but minimal no calculate even G always, conflict. proper is central each [AL421 it Fig. a conflict symptom 2 Initial combinatorics of set must also be a be represented conconIlicts, where a subset which is also to the performance corresponds to a as well. Notice property that didate candidate that any the candidates l;~tlicf: working empty set, 2. the space can rccoguition of t,he have must represented the is the the the single root the sll )sct,-snI)cl.,~ct tltcn of the flcfitie is a might be is the at the bottom is constructed candidate observations a up candidate lattice and construct minimal Idie minimal the a can- is to idcnThe space of boundary below is not. every component fro~il set of candidates to of a camflitl~~lcs recognition uses device be consistent by in tcrri1s tnirliurnl thus conflict ConIlict concisely candidates of all candidates be t.hat f~vcrytling ;vhilc: everything measurements [], which a model ic ‘1‘1 summarize, stages: example. of a candidate be vislinlixed correctly, of Fig. circuit goal of candidate generation set of niinimnl candidates. 2). bonritli~ry suc!i vnlifl candidate, Given no for be represented conflicts, superset Thus can (Fig. To like observations candidates. The tify the complctc space candidates that, as well. with two / SCIENCE IMU9 &i%All = though procedure. The goal of conflict recognithe complete set of minimal conIlicts.3 not PfMwI [Mull of conflicts be repreIf a set of components the set of conflicts can identifying the minimal is minimal D = 3, correctly It is essential the of that every and For symptom can give the set of all com- superset then 12, single including is a conflict, 134 must nents, MS). set ‘JJyJ>ically, symptom candidate thus starting produces that the concisely. Thus by only are However, it still any A2 we to ponents in the circuit. diagnosis it is essential sented and manipulated single hold. ac- A = 3, C = 2, AZ, Ml, and MS, (i.e., AZ, Ml, For complex domains to a large set of comlicts, conflict to the inputs functioning prediction, second: and 12. G to be 10, the = G is measured the the M2, assuming arc when with conflict. cisely other agree with one prediction resulting in a symptom. inputs B = 2, C = 2, assuming 3, and Thus, based might another, with the 3, <and 1W2) agrees any fail Every how [MI.M2.Ml.Al.A2) we calculate observation F ignoring 10. and every [...I). for Ultimately, the goal the set of candidates at F. funct,ioning with the and AZ), Al) A measurement yet disagree with example, starting and M’2, Ai, must observations is represented by For calcu- are conflicts as well; however, M;!, Al) are necessarily confticts since in the conflict were necessary to constrain subsets of (Ml, the components the (Ml, the hypothesis the model. and refine, conflict. 12 depends on the correct operation of i.e., if Ml, A42, and A, were correctly F = 12. Since F is not 12, at least one the set example of Ml, M2 and Al is faulted. Thus the set (Mi,M2,Ai) (conIlicts are indicated by (...)) is a conflict for the synlptom. Because the inference is monotonic with the set assumptions, is a particular differs from is to identify, made a complete in generation. along set with of min- imal conflicts. minimal candidates. Next, candidate conflicts to Candidate section, While generation conflict recognition [&fz]; the of minimal of the next imal. Ijowever, in Section [W, the uses construct a complete generation is the the set topic is discussed set of we must 7. 0. Candidate Diagnosis is an incremental measurements process; takes didate ments. space and then uses this Within a single diagnostic candidates must to having the through decrease the the total corresponds set of conflicts to having tonically down the conflict generated old to generate The set of candidates lows. Whenever a new minimal flict are candidate is replaced by one minimal based on complished minimal which by intersection the up along the new Eliminated from added to the thus explains all single those = 10 Mz). This rules its immediate and [AZ] are and [A,] explain examined. the superset the three candidates minimal does not explain ate superset candidates the new new and minimal thus 12” the old When are any which candidates are new already All are implicitly set that one minimal [Mz], candidate [MS], [Al], are their [AZ, MS] discovered [Mz], recorded; immediate are supersets above. [AZ, however, of the its three represented. consists the conflict [MI], thus of supersets candidate seen candidates conflict, are no conflicts, working) produces and not. for there are everything single [Ml], of the do except candidates the have not conflict [MS] candidates We out the supersets Each and which is ac- non-empty. remaining Initially [] (i.e., observations. [AZ] is of MS] immediminimal Therefore, of [Ml], [Mz], and [AI]. 1% The second (4, A2, Ml, conflict MA (infcrrcd only eliminates from observation minimal [p/f2, M3], howcvcr, resulting [Ml], [AZ, Mz], Candjdate of [Mz]: of these [A,,MzJ in the and [Mz, generation remain and surements never minimal once eliminated accumulate Second, decrease. [A2, M21 [MI], [A2, candidate candidate (and thus assumption that there is necessarily is only a single never model-based troubleshooting every false. fault SU- n/iz], and set: [Al], proper- increase however, or a AS mea- reappear. minimal candidates appears in every candidate), then Third, the (exploited that presupposition in all strategies), assuming all candidates set of candidates can are respec- interesting the sizes of the if an assumption n/r,], explains [n/r,, ad are several can min- candidates [Al, Mz], candidates Il;inimal Mz]. has [ tll] of minimal ties: First, the set of minimal candidates may decrease in size as a result of a measurement; G == Catldidid! Qualitative 7. con- candidates. (A,, Ml, [I. Thus, however, new from recorded the conflict, aud set previous is equivalent are singletons. be obtained by In this intersecting to case, all the the conflicts. the lattice the as folprevious with more than one parent be found along each branch. example. candidate “F care the first candidate reached i.e., when the candidate’s candidates set of new symptom towards modified any explain the the or duplicated; Consider our the minimal lattice candidate(s). conflict past a candidate candidate must subsumed This mono- or more superset candidates this new information. This moving up a consistent are new not recording new conflict; with candi- Similarly, empty set. Candidates the new conflict(s) and does moving candidate, explains the the [Ml], complete of the minimal candidates [Al] Thus the new minimal candidates candidate, up monotonically. conflicts move is incrementally conflict is discovered, which of corresponds towards superset by the using measuretotal set components. a conflict represented incrementally, candidate(s) of all must increase the minimal through new can- monotonically lattice set amgnosthe This move superset by as the relines to guide further session the candidates candidate represented continually monotonically. minimal the date he candidntcs to consider the supersets Each an d [M2, M3]. M2], persets tjvely. Generation tician unaffected Recognition tion we first This approach A conflict sumptions, they are then the quires present a simple to an inference ENV, In example, approach made thus whether the after is refined Refinement of to the At each I: minimal minimal vironment, recognition. lo}, and given the combination F = {M~,M2,A,}) the conflict the cnvironis consis- 10, and (Ml, before (leaving M2, Al). as follows: Exploiting minimality. To environments we begin up along pattern environment strategy. a set of as- which far, measuring inconsistent conflicts), moving search of conflict C(OBS,ENV) OBS measuring G = 12, C({F = off the inputs) is false indicating This constructIn this sec- as an environment, and testing if with the observations.4 If they are, environment is a conflict. This re- determines our model strategy of observations tent. incrementally generation. is then refined into an efficient can be identified by selecting referred inconsistent inconsistent merit set Strategy The remaining task involves the conflicts used by candidate ing set Conflict our its used search parents. during we apply identify (and at the This candidate the thus the empty enis sinlilar generation. C(OBS,ENV) to dcterrnine 4 An environment should not be confused with n calldidnte. An environment is n set of assumptions all of which are assnrned to be true (e.g., A41 alld M2 WC CSSIII~~CI to bc working correctly), a cnndidntc is a set of assumptions all of which arc assumed to be false (e.g., colllpollents Ml and A42 are liot fuuctionillg correctly). at least one of which is f&c. A conflict is n, set of ;lssun~~~l.iolls, Intuitivrly an rnvironmtmt is t,bc% set of assulllptions tlmt defijl:? il "contc!ut" in a deductive infc~rcrtcc engin(B, in this cnx: t,llc engilM2 i:; IISCX~ for particular Reasoning pdict.iotr n~otlcl-artifact and Diagnosis: and t.ho assurnpt,iom ;IIC ;hout the 1:lck of dilfcrctlccs. AUTOMATED REASONING / 13 j whether or not ment ENV is explored, is a conflict. all other of the new environment ronment is inconsistent supersets are been explored run on the We must then environment presume hypothetical For example, Mz}) produces OBS are = the circuits 8. ceding for plementation made). which To vnri- modified ENV. be implemented in terms of P. Refinement 2: puts our are kept knowledge constant, of the cally. Given a new a superset the of every values only of infer Refinement ically with from tion an when the incremental each {Al,Ml, = 6, Y = a datum is completely If P com- the simply measurement for environment. If a set Analomonoton- on every possible Inferences. environment. and over can the same antecedents. by utilizing ideas the same rule we will Refinement an cessful. ignore enviromnent inferences ronment absent in every a new be faced bcr of potential practice, with this empirical only assumption 136 I SCIENCE are over and of this overlap Maintenance as a dependency [ 71. This previous doesn’t one of its unique a computation exponential are weakly connected. property. For of interest weakly Our example, will G two supporting Any set the strategy depends If the in electronics of components are performed inference For proce- example, forms prediction, many of resolution V, it follows call this the set of envi- (i.e., ENVS(V) set the support- Exploiting the agenda the IllClJi,, B correctly. Thus, {Mz}. Second, Y = F = = 6 X x 15) = 6 assuming one of its supporting G - 2 = G - (C working. Therefore inference x the are for enviroamcnl on ;dwnys oho lhat in their property that whenever a datum first. Whenever is marked or:‘* -iron nlcut. dctlucetl dcsircd this. are A simple a symptom a conflict Using this nlinilual th,at created effect of computational producing is performed srlIfices lJlCChc?~JiSlll any one done changes, consequents such the is ever a fact dcpcndencies achieves the IJrixCSS posslhle, environment the the l’his first, run. without incurring the stops of of its by tracing was rule smaller no inference environment environments are facts calculate Y := 6 are {{Mz}{A:!, MS}). to derive Y = G is a superset m dependencies supporting control ;Lcllicving can = ,‘l13 nre min- measurements we Y monothe two. rule the inf::ro~icing schr~lle First, supporting is rccoguizcd, the case of used is controlled that the in- framework. after 112 and inFerenccs in the the ways. in propagation, demon qualitative simulations, which We this automatically Wc two in- In exploitin, updated envi- connected, example of these By overhead, in our environments of assumptions interesting However, be con- dependencies. Al prediction. 12. I;-‘) == 6 assl~ming when the rerunning num- = n/r, is functioning environnlents is then we would every from env)}). of the different strategy to its name) in the be sets and twice. If every then differences, components sets subsets. inference, with can be ext.ernally we presume many general main- processing, whenever permissible, that Most and this ENVS(V), E P(OBS, Consider 10 of one care suc- some associate these criteria. systems, be truth or disbelief inferences general fit environments and is primarily refinements contain model-artifact as the rules be executed four refinements allow the extent of not even generating which run set locality. the contained still ferences Exploiting of why The first (i.e., to the any on 5: observation be a large All Truth these order can Finally, can tonicity property, it is only necessary to represent imal (under subset) supportiug environments. in need of such that every inference is recorded no inference is ever performed twice very P must Thus, of data-bases, again on be avoided of P(OBS,ENV) been analyzed. We ing follow computation have already Redundant by is monotonic. four deduction ronments, E {envlV ENV. simple 4: procedure these belief that, during is simultaneously itn- (or utilizing (2) pre- t, Ihe meels justification) and determined ference of typ- in the augmcn P for (i.e., in which theorem-proving then the addition of any assumponly expands this set. Therefore P(OBS,E) for every subset E of Refinement criteria inference, and that this our architecture). meet that natural environment, to that environment contains P(OBS,ENV) This makes the if all its subsets presume basic irrelevant (i.e., by dures colllporlent discussed and cache set of predictions order ideas moclify ENV) the grows two the to we presume one inference actual interact SO that Architecture expert rule-based systems, constraint invocation, taxonomic reasoning, is made assumptions. set of predictions We A dependency If in- to of P. (1) if we addition need for P(OBSU{M}, a new exploit we structed cumulative and grows monotoni- Thus Monotonicity 2, the the M, P(OBS,ENV). P, 3: to refinement are completely tenance: more signals designed Procedure the of measurements. measurements circuit’s structure measurement is always we need predictions. Monotonicity (or Inference In addition, more than whose limited. section Let follow 12). now explicitly to) assumptions two distinct values for a quantity z and - TC), then ENV is a conflict. and are are entirely values connected interactions explored. 3,B = 2,C = 2,D = 3}, 3, B = 2, C == 2,D = 3,X = are ically not observations predictions given which a subset operates (e.g., given the behavioral environ- has already then C is not strategy predictions P({A {A C can gous are first. If the enviconflict and its its supersets inference environments P(OBS,ENV) b e all from the observations putes both be explored it is a minimal and the in 6, F = a new which not explored. If an environment or is a superset of a conflict by inferring ables Before environments only and all control environminimal conflicts (i.e., In this usually inconsistent environments) architecture is). The P can only fewer conflicts will will be eliminated mistakenly arc geueratcd. be incomplete consequence trigger (in praclice of incolnplcteness be detected and thus than the ideal - no it is that fewer candidates candidate will be taken. far we Thus have handling domain the P. During the the power of this function demonstrate the problem For our of circuit example suppositions. only we in terms of each type thus some values value the model must be every are terminal from other values points, using the values. The application assumption that working correctly. If two values quantity its in different ways, then component of each corresponding are component deduced a coincidence differ then the coincidence consists of every component throng11 from the measurement cidence (i.e., the sympt,om used points implies to deduce the two the has occurred. point at least values same C to constraint cells: con- some unas- of insufficient it can also =arise in the of each For tcrmi- cells propa- component example, in analya- are are circuits, 0 and t, and model A, for and are the cells the constraints the repre- circuit of Fig. D, E, X, Y, 2, F, and the observed values: A = 3, B, C, provided = 2, D = 3 and E = 3. There are three and two adders each of which is modeled by MI CXE, following constraint process and behavior In digital values constraint: : 2 = by used the cells represent circuit voltages are numbers, and the constraints the of which a single MS values inconsistency incompleteness the ten AI : X = A x C, M, : Y = I3 x D, and A2 : G = Y+Z. The : F = X+Y, is a list of cleductions propagator generates (component and dependencies (a dependency that the is indicated : antecedents): is a symptom. propagated to the that, is for are B = 2, lnultipliers to deis based in a parhave different propagation domain, equations. There G, five the recognized from the models model If the two values The conflict then components and Intuitively, symptoms are locally through components can cells out inputs are unused of logical circuits the values Consider 1. Instead, two dependencies constraints sent logic levels, the are boolean equations. expensive, is known. trace the usually arises as a consequence about device inputs. However, mathematical Fi- values to other a logical as a set of constraints. ing analog currents, is whether when constraints which inputs (i.e., constraint some circuit is modeled correctly. of measurements the cell event consequence In the pre- from same the leaving as the gator. of a circuit considered is working in terlns inferred WC it to Thus, along conflict. signed. This informatiou plus a behavioral Second, that the Measurements at component models. by propagating out on the the In this the nates deduced. is manifcstcd for Sometimes paper, applying of simplifying difference component are made terminals. every measurement duce new that this by locally ,i synlptom diagnos- whose applicathe selection of of approach, assume of model-artifact not remainder of a circuit topology of its components. or not a particular nally, all observations at a component’s general faults: only on diagnosis. we make a number First, is described description a very multiple depends to be of conduits The dependencies recorded through the constraints that deduced struct descrihcd tic strategy for tion to a specific out is identified). Diagnosis values as a set the system. ticular path are Circuit more be viewed be propagated eliminated. 9. and may X=6(MI:A=3,C=2) of coinone Y=6(M2:B=2,D=3) of the is inconsistent,). Z=6 (M3:C=2,E=3) F=12(Al:X=6,Y=6) 10. Constraint Constraint ues, and propagation constraints. as voltages, Propagation Cells represent state constraint isfies propagation the that viously unknown values v = 2 and to calculate records the If’s newly The allows variables cell. i = value a set each such basic For 1, then R = a value inference step on value a value example, it rises 2. In if it the cnusc the the when measuring conflict(s) symptom (A,, has some important necessary for the find to be inputs v = iR 1~ -z ill. conslrnints Qualitative to at, any or outputs point in the taken. tions Scconcl, about Ilie ncnts. Tn most inputs to constructed Reasoning and it is not direction digital outputs. by Diagnosis: where detcrnlincd AUTOMATED Each the properties. circuit. First, of these A path can only a subtracI.or xi input begin has lo make any flow tliroiigh a signal paths may a measurement cxa~l~plc, reversing 12). example in this points necessary that sigllnls circuits For sinlply starting of the circuit are MI, Mz)), a conflict is to propa.gnt,or (e.g., indicates is not discovered values F to be 10 not approach it a pre- two This sat- constraitit other is indicated same cell (e.g., leads to new that for has A symptom for the symptom values, constraint addition, 21, i and ~lrny of initial cell it to determine depclltloricy recorded Given assigns constraints. a constraint The R. G=12(AZ:Y=6,Z=6) val- example, among V, i, and flows. cells, lates a condition that the cells must satisfy. For Ohm’s law, ZI = iR, is represented as a constraint cells or fluid on stipu- three levels, operates A constraint the logic [12,13] been assumyconipoflow from cannot ,and REASONING the bc output / 137 of an adder since However, the irrelevant to our a constraint be any mation diagnostic direction. function values desired. flow directionality of signal technique: the way can the of a component’s between used infer it violates directionality along a path terminals its inputs must places can which discrepancies, infor- through a component in any have subtractor its outputs D= E= F= is detect For example, although the in reverse, when we observe what flow a component of its To flow. signal does we 3,0 390 lO,{} G= 12,{} X = 4, (AI, j&&G, Ad&) ww not can Y I= {Al, 4, M,} %{M2}{Az,M3) been. Z = 8, {&,~MI) 6,{M3}{A2,M2) 11. Generalized Each step tecedent of constraint values a constraint which and each step crementally our We are first. ensure This assertion x with any the its associated measurements the inputs, [B = 2, {}], [[C = Observe that when nent, the assumption dependency, the and thereby supporting propagated place, to the supporting Propagating value. = 6, {Ml}]. gations produce: 12, {AI, wafdn, [[Y = 6, w2)n, uz = 6, w3n and [G = 12, (A2, M2, it&}]. This adds measure F to be 10. Suppose 10, {}I we to t,he (starting 4, {Al,M2}~, between [[F ognized Thns tion smaller in the Next, tom “G MS). The A= B= c= = Ml, Mz} goes one more step: are no more inferences = MIIII, 12 not final we measure 6, {A2,M3}], and [X = 4, 10” produces data-base state G as Given measurements further and its be 12. approach structs Truth Maintenance [IF = straint module, = the and propagasupersets. AZ, MI, Propaga- agenda approach are structures base state uate hypothetical [Y = 6, {&,~I~}], 12 = {A 1, A2, &}]. The sympthe conflict (Al,A,, Ml, all minimal is:5 ure / SCIENCE within The first and conmini- the the third con- is a general two modules. incrementally minimal the model-based measurements and con- The last constructs conflicts. paradigm, our potential model- are given. In [3] we exploit the framein two ways to generate measurements optimal. constructed of Section 11) by our make environments, strategy it easy measurements. (e.g., the data and eval- as we construct and to compare information First, the to consider Second, conflicts, relatively straight forward ments (using probabilistic candidates, it is potential measureof component fail- rates). The wards work Ihe remains order candi- implemented implementation maintains information-theoretically goal much 1) incorpornling 138 from work differences of this paper data propagathe for each prediction and It is based on Assumption-Based of [5]. presumes which completely on the generator, candidates As all the artifact work been architectures language based the candidate minimal the until [4]. Tl le second controls the inference conflicts are discovered first and records of inferences. It is based on the consumer such that minimal the dependencies = 6 uses the two minthe set of mini- continues constrained examples. Our modules. The first environments conflicts. minimal effort Research has mal supporting 3,0 2,{) 2,o has generation cycle been sufficiently tested on numerous sists of four basic up [[X [G = 10, {Al, to be made. to candidates. new is conflicts. mal Our follows propagation in section construct 12. Connected propa- first): during non-minimal discussed incrementally given NOW the symptom {Al, Ml,M2}~ is recconflict: (Al, MI, M2). prevents {Al, environment [I2 A2, sets minimal architecture suppose gives: 8, (4, a new inference proceeds assumption = 4, {Al,Ml}j. and [TF = 12, and [Y = 10, {}I indicating the Analysis base. the The propagation MS}]. There tion data with remaining point The algorithm conflicts to [A = 3, {}I, environment(s) and C through A The no imal environments. take of: [[X we and the at in constructing tion/candidate date space 2, {>], [[D = 3, -OD, and UE = 3, Onpropagating values through a compofor the component is added to the thus that wasted guar- obtain: Ml Note to infor propagations first, or propagations data base consists conflicts: (AI, 4, Mdf3) guides that the resulting supporting environments arc minimal. We use 15, el, e2, . ..I to represent Before minimal built manner candidates that performed in two architecture in an efficient conflicts and example. results a set of anWe have inference environments environments anteeing conflicts of our during propagation construct minimal in subset takes a consequent. within minimal multiple faults. Consider Propagation propagation computes propagator explores only Constraint This to diagnosis presented here of automatecl to be the done. represents diagnosis, Plans predictive systcn~s for cnginc with <another step nevcrthcless the future cliscussed time-varying tothere include: in signals [14] in and state, and 2) controlling ences being considered. the set of model-artifact differ- 10. Mitchell, T., learning, 78-711, 13. Related R., Artificial This research fits within the model-based paradigm: propose [1,2,3,6,8, 9,111. However, a general method of diagnostic effic.ient, incremental, extended has to been include these account of many recognition and faults, and strategies. ideas Reiter independently of our and “intuitive” candidate is easily (111 (University 12. Steele, AI of generation. Ramesh cially Patil, thank Ray productive Randy Halasz, provided Reiter for Davis, Walter useful his “Doing G.L., Time: Penn., and Diagnosis: (August, first principles, Also: Depart- Report 187/86, 1985). implementation of a based on constraints, Cambridge, MA, 1979. A CONSTRAINTS: descripl-39. Putting Proceedings Intelligence, Qualitative of the NuPhiladel- 1984). Kenneth Forbus, Hamscher, Tad insights. clear phia, concept STAN-CS1978. almost-hierarchical 14 (1980) Reasoning on Firmer Ground,” tional Conference on Artificial ACKNOWLEDGMENTS Hogg, MIT, Steele, expressing Intelligence B.C., from Technical Toronto, 595, and language for tions, Artificial 14. Williams, Daniel G. Bobrow, Matthew Ginsberg, Frank Science Report G.J. to forthcomming. G.L., The dcEnition and programming language Technical approach Department, University, of diagnosis of Toronto, 13. Sussman, techniques A theory of Computer computer provides Science Standford Intelligence, ment unlike [1,2,6,8, 91, we reasoning which is multiple measurement exploring a formal conIlict handles debugging An spaces: Computer Palo Alto: 11. Reiter, Work Version We perspective espe- <and many interactions. BIBLIOGRAPHY 1. Brown, gogical, J.S., Burton, R. natural language techniques in and J.S. (Academic 2. Davis, SOPHIE I, II Brown (Eds.), Press, New R., Shrobe, Shirley, M. and tion of structure National PA (August, W., Kleer, 7. J., Doyle, Local J., A truth telligence 8. Williams, 24 B.C., Intelligence assumption-based circuits, Cambridge: Pitts- 137-142. system, Artificial Intelligence de Kleer, J., Problem solving electronic AIM-394, K., Intelligence, Artificial 5. de Wieckert, on 4. 6. 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