R. Davis. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24:347--410, 1984.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....diagnosis using fault trees [2] are based on common failures that the operators have experienced either real system operation or training. As a result those system are unable to tackle new faults. However, a diagnostic system which reasons from the knowledge of structure, function and behaviour [3] of the plant, can either endeavour to overcome unanticipated situation or degrade gracefully. Secondly, qualitative models can be implemented in the KB so that common sense reasoning about a physical system can be simulated. This greatly helps in explaining the path of reasoning to the operator. ....

....common sense reasoning about a physical system can be simulated. This greatly helps in explaining the path of reasoning to the operator. Second generation Knowledge based system utilizes KB and reasoning concepts that are general and qualitative. Reasoning from structre, function and behaviour [3], causal reasoning [4] qualitative physics [5] multi level multi model reasoning [6] model based reasoning [7] and heirarchical reasoning [1] are some of the second generation reasoning paradigms. These pardigms can be applied to various problems includeing fault diagnosis for process plants. ....

. R. Davis, "Diagnostic reasoning based on structure and behaviour", Artif. Intell. vol.24, 347-410, 1984.

....missing matrix elements allows us to remove the difficulty commonly associated with heuristic systems, namely: a system based upon empirical associations is more difficult to construct because the character of the knowledge makes it necessary to extract the rules on a a case by case basis. [17] (page 391) Using matrices, it is easy to support knowledge acquisition from multiple domain experts. In the grid generation domain, three domain experts were used concurrently; each identified the rules of separate columns of the K matrix. Similarities exist between the matrices used by HFC and ....

....a heuristic classification rule. HFC leverages both the heuristic classification approach of capturing empirical associations and the model based reasoning component paradigm. HFC views the debate of when where to utilize heuristic classification [11] vs. first principles qualitative constraints [17] as analogous to the debate in mathematical modelling of when where to utilize models defined as curve fits to empirical data vs. utilizing models defined from first principles. The dot product rules can be viewed as operational rule models [15] Rule models are abstract descriptions of subsets ....

R. Davis. Diagnostic Reasoning Based on Structure and Behavior. Artificial Intelligence, 24:347-410, 1984.

.... to the basic DPLL procedure have been proposed, including smart branching selection heuristics [9] randomized restarts [10] and clause learning [11 15] The last of these, which this paper attempts to exploit more effectively, originated from earlier work on explanation based learning (EBL) [16 19] and has resulted in tremendous improvement in performance on many useful problem classes. It works by adding a new clause to the set of given clauses ( learning this clause) whenever the DPLL procedure fails on a partial assignment and needs to backtrack. This typically saves work later in the ....

Davis, R.: Diagnostic reasoning based on structure and behavior. Artificial Intelligence 24 (1984) 347--410

.... # Research supported by NSF Grant ITR 0219468 mance of backtrack search algorithms by generating explanations for failure (backtrack) points, and then adding the explanations as new constraints on the original problem [de Kleer and Williams, 1987; Stallman and Sussman, 1977; Genesereth, 1984; Davis, 1984] For general constraint satisfaction problems the explanations are called conflicts or no goods ; in the case of Boolean CNF satisfiability, the technique becomes clause learning. A series of researchers [Bayardo Jr. and Schrag, 1997; Marques Silva and Sakallah, 1996; Zhang, 1997; Moskewicz ....

R. Davis, Diagnostic reasoning based on structure and behavior, J. AI, 24 (1984), 347--410.

....voter circuit from [Isc85] Multiple Views. Multiple views allow to describe the diagnosed systems emphasizing different aspects. For circuit diagnosis it is often important to consider a physical view beside a functional one, because the physical view additionally takes the layout into account [Dav84]. We want to employ the functional model by default and the physical model only if we do not obtain good diagnoses. Strategy (1) tells us how to choose between the models using the hypotheses force physical and force functional. The predicate implausible, which in our example holds if no single ....

....model. The functional model is used by default when no hypothesis is active: force functional :force physical functional force functional functional; force physical physical Structural Refinement. Many authors address the use of hierarchies to reduce the complexity of diagnosis problems [Dav84, Ham91, Gen84, Moz91, BD94]. In particular, Bottcher and Dressler introduce the strategy of structural refinement which states that an abstract model of a component is refined only if it is uniquely identified as defective [BD94] Only if all diagnoses contain a component C, it is possible and necessary to activate a ....

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R. Davis. Diagnostic reasoning based on structure and behaviour. Artificial Intelligence, 24:347--410, 1984.

....the refined quantitative descriptions of surviving behaviors are precisely what is needed for differential diagnosis, for example by selecting a quantity whose ranges in two different behaviors are non overlapping, and testing for its value. The work on diagnosis from first principles by Davis [1984], Genesereth [1984] and Reiter [1987] provides methods for optimizing the selection of new tests. It should also be possible to perform a sensitivity anal ysis [Raiffa, 1970] on the results of the propagation, to assess the sensitivity of Q2 s conclusions to variations in the quantitative ....

Randall Davis. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 1984, 24: pages 347-410.

....with suggestions for future research. 3 Related Research Model Based Diagnosis is a symptom directed technique; it is driven by the detection of discrepancies between the observations of actual behavior and the predictions of a model of the system. Almost all of the reported work in the area [2, 1, 3, 4, 5, 8] has been concerned with the diagnosis of physical systems subject to routine breakdown. Model based diagnostic systems use simulation models that compute expected outputs given known inputs; they utilize dependency directed techniques to link each intermediate and final value to the selected ....

Randall Davis, `Diagnostic reasoning based on structure and behavior', Artificial Intelligence, 24, 347--410, (December 1984).

....analysis and diagnostics. It gives the up states which define reliability and its complement gives the possible states explaining the violation of the reliability specification, i.e. possible diagnostics. It is well known in model based diagnostics that such conflict sets play a key role [23, 10, 19]. The duality implies that they play an equally important role for model based reliability. If the structure function # #,# is monotone, then to the minimal possible arguments # PL(#, #) correspond the minimal cuts #. They represent minimal sets of failed components, which explain the ....

R. Davis, `Diagnostic reasoning based on structure and behaviour ', Artif. Intell., 24, 347--410, (1984).

....associations is often employed to denote such knowledge. The classical example of such a system is MYCIN [Shortliffe, 1976] The model based approach to diagnosis has been successfully applied to fault finding in electronic circuits. Early work in this field is described in [Brown et al. 1982] [Davis, 1984], Genesereth, 1984] and [De Kleer, 1976] The study of simple electronic circuits has yielded much insight into the nature of the diagnostic process. More importantly, one of the first formal theories of diagnosis emerged from this research: the theory of consistency based diagnosis as proposed ....

....by findings. If there is a discrepancy between the observed and the predicted behaviour, diagnostic problem solving amounts to isolating the components in the device that are not properly functioning, using a model of the normal structure and behaviour of the device [Brown et al. 1982; Davis, 1984; Davis Hamscher, 1988; Genesereth, 1984; De Kleer, 1976] In doing so, it is assumed that the model of normal structure and behaviour is suciently accurate and correct. Figure 2 depicts DNSB diagnosis in a schematic way. DNSB diagnosis is frequently erroneously called model based diagnosis in ....

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R. Davis (1984). Diagnostic reasoning based on structure and behavior. Artifi- cial Intelligence, 24, 347-410.

....of more than one explanation will have different effects according to the task being addressed, sometimes indicative, sometimes benign and sometimes detrimental. Abductive reasoning has been used to good effect in model based diagnosis, where possible explanations of malfunction must be formed [3, 5]. 5 Using Abduction for Sensor Data Assimilation, Map building, Planning and Localisation This section considers how the abductive reasoning scheme might be applied to a range of different tasks. In each case the robot controller is presented with some event (styled G ) either actual, as in ....

Davis, R. (1984) "Diagnostic Reasoning Based on Structure and Behavior", Artificial Intelligence, Vol. 24, pages 347-410

....function calls that compute the result. Any one of these calls could be responsible for the bug. The number of suspects can be further narrowed by appealing to the following pruning method, which is closely related to the constraint suspension technique for hardware trou bleshooting described in [15]. Assuming that the current manifestation is due to a single bug (this is not the same as assuming there is only one bug anywhere in the program, which is certainly not very plausible for a large system) if every possible output of the suspect leads to an erroneous result (including abnormal ....

R. Davis. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24(1-3):347-410, December 1984.

....annealing. Constraint Satisfaction A special form of inference is realized by constraint satisfaction techniques, which has been applied successfully in many subfields of AI (such as computer vision [Waltz, 1972] circuit analysis [Stallman and Sussman, 1977] planning [Stefik, 1981] diagnosis [Davis, 1984] [Geffner and Pearl, 1987] DeKleer and Williams, 1986] and logic programming [Jaffar and Lassez, 1987] and among which are connectionist approaches as well. Constraint satisfaction may be described as follows: given a set of variables and a set of constraining relations on subsets of these ....

R. Davis. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24:347--410, 1984.

....du syst eme observ e avec celles d un mod ele qui evolue de facon synchrone, c est a dire en temps r eel, avec le syst eme. De meme, la tendance actuelle est de fonder les phases de localisation et de diagnostic sur un mod ele profond qui d ecrit la structure et ou le comportement du syst eme [5, 6, 7, 15]. Les r eseaux de Petri sont l un des mod eles les plus utilis es lorsqu il s agit de syst emes a ev enements discrets [4, 8, 13] Ils sont en effet, bien adapt es pour d ecrire la dynamique de tels syst emes (description des passages d un etat a un autre) Par contre, ils ne correspondent ....

R. Davis: "Diagnostic Reasoning Based on Structure and Behavior". Artificial Intelligence, vol. 24, 1984, pp: 347-410.

....a reference node) Our algorithm is efficient in the time needed to produce the model and in the size of the resulting model. The size of the resulting model is important, since it will impact the reasoning tasks that follow, specially constraint propagation, which has been shown to be intractable [3]. In section 2, we give the basic background that supports our algorithm. Section 3 outlines the proposed algorithms. Section 4 presents an application example. Section 5 presents some of the related work in this area. Section 6 concludes the work presenting our main contributions and discusses ....

....In the limit, all values are precisely specified (i.e. all variables take on real values) In that case, our results match exactly with those specified by any traditional circuit analyzer. 6. Related Work There have been many successful effort to perform model based reasoning about circuits [18, 19, 4, 3, 11, 13, 6, 8, 16]. The most recent work and more related to this one are the works of Flores and Farley, and Mauss. The work of Flores and Farley can only cope with series parallel reducible circuits, missing an important number of circuits and being of no much use for many practical applications. For instance, to ....

R. Davis. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24:347--410, 1984.

....reference node) Our algorithm is efficient in the time needed to produce the model and in the size of the resulting model. The size of the resulting model is important, since it will impact the reasoning tasks that follow, specially constraint propagation, which has been shown to be intractable [Dav84] In section 2, we give the basic background that supports our algorithm. Section 3 outlines the proposed algorithms. Section 4 presents an application example. Section 5 presents some of the related work in this area. Section 6 concludes the work presenting our main contributions and discusses ....

....its children are the elements of the star, and its ancestors, S nodes, represent the Kn(n) that replaces the star. 9 Figure 5: Final Reduction Graph for Circuit of Figure 4 5 Related Work There have been many successful effort to perform model based reasoning about circuits [SS77, SS80, de 84, Dav84, Gen84, Ham91, FF96, Flo97, Mau98] The most recent work and more related to this one are the works of Flores and Farley, and Mauss. The work of Flores and Farley can only cope with series parallel reducible circuits, missing an important number of circuits and being of no much use for many ....

R. Davis. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24:347--410, 1984.

....of non linear circuits by representing each circuit element by its different operating regions, and switching models when crossing boundaries. In the area of diagnosis, although there are quite a few publications, most of them deal with digital circuits, or DC non linear circuits. Randall Davis [9] presents a methodology for diagnosis of digital circuits. His structural representation includes electrical, physical, thermal, and electro magnetic adjacency; he claims that many of the faults are not only due to misbehavior inside an element, but also to interaction among elements. He develops ....

R. Davis. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24:347--410, 1984.

....if the valve is connected to a container holding liquid, then opening the valve may cause other indirect changes in the world, e.g. the amount of liquid in the container may change. In either case there are qualitatively understandable in uences between objects which are adjacent in some sense [2]: physical force applied to the valve causes a movement in its parts; a liquid which is in contact with the opening in the container will ow out of it. For example suppose the pms has the goal of completely lling container2 with water. Container2 is currently empty, but is connected by 25 a ....

R. Davis. Diagnostic Reasoning based on Structure and Behaviour. Articial Intelligence, 24:347-410, 1983.

.... Delta f r for qs( then theorem 1 implies that qs(h) h 1 Delta Delta Delta h k ) f 1 Delta Delta Delta f r ) and one of the decomposition algorithms mentioned above can be used. 3 Example Figure 1 shows a network of adders and multipliers, a classical example introduced by Davis [13]. Using the in 11 =3 in 21 =2 in 12 =3 in 22 =2 in 13 =3 in 23 =2 v 1 v 2 v 3 out 1 =10 out 2 =12 M 1 M 2 M 3 A 1 A 2 Figure 1: An arithmetic network. unary predicate AB( the behaviour of the adders and multipliers can be described like ADDER(a) AB(a) Gamma out(a) in 1 ....

R. Davis. Diagnostic reasoning based on structure and behaviour. Artificial Intelligence, Elsevier Science Publisher B.V. (Amsterdam), 24:347--410, 1984.

.... to a human user [31, 42, 43] 8 In addition to their role in communication, causal explanations play a central role in focusing other forms of reasoning [47] Causal explanations are used in diagnosis to focus the reasoning only on those elements that could have caused a particular symptom [10]. Causal explanations focus design and redesign by focusing the reasoning on just those mechanisms that can produce the desired behavior [50] Causal explanations can also guide quantitative analysis by providing an overall structure for solving the problem at hand [11] 3.1 Causal explanations ....

....the above two results in much the same way that Theorem 6 was derived: Theorem 8 Theorem 6 continues to hold if only self regulating differential equations can be equilibrated. 7 Related work One of the original inspirations for the work described here was Davis s work on model based diagnosis [10]. In that work, Davis presents a diagnostic method based on tracing paths of causal interactions. He argues that the power of the approach stems not from the specific diagnostic method, but from the model which specifies the allowed paths of causal interaction. He shows that efficient diagnosis, ....

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R. Davis. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24:347--410, 1984.

....a specific subset of these relations. That is, we can automatically generate the main structure of the global ontology by exhaustively seeking the arguments of these sine qua non relationships. TERMINOLOGY The activity of knowledge representation can be conceptualized in terms of several roles (Davis et al. 1993), one of which is fragmentary theory of intelligent reasoning. In this paper, however, we do not investigate how to best represent the heuristic or algorithmic knowledge, but solely focus on how to provide an understanding of declarative human conceptual knowledge for computers. Our task is to ....

....ontological world. In this case, the union of new conceptualization (i.e. new entities along with their ontological interrelations) and the existing global ontology would lead to the new state of the ontological network. In diagnostic reasoning, model selection is a major problem (Weld, 1990; Davis, 1993). If we state the diagnostic problem in terms of a set of variables and constraints (i.e. ontological commitments) then the unification of the problem set and the general set of entities and qualities of the ontological network (i.e. the whole conceptual universe) would result in a definite ....

Davis, R. (1993). Retrospective on "diagnostic reasoning based on structure and behavior". Artificial Intelligence, 59(1-2):149--157.

....combine them [34] 44] HAMS 91] STDR89] Abductive approaches have been introduced to cope with a system description based on fault models. In its original definition abductive diagnosis implies the availability of a complete representation of possible faults. In the consistency based approach [18][33] 20] 41] DAVI84] GENE84] DKWI87] REIT87] the diagnostic space (i.e. the space of all the possible diagnoses) is pruned by the consistency checks between the model of the correct functioning of the device and the observed behavior. Advantages and disadvantages of this approach have been ....

[DAVI84]R.Davis "Diagnostic reasoning based on structure and behavior" Artificial Intelligence, vol. 24, pp.347 - 410, 1994.

....the QDE come partly from the constraints in the individual components, and partly from the connections among terminals. The Closed World Assumption is speci ed by the modelbuilder in the nite set of components and connections. Component connection models are also useful for model based diagnosis [7, 9], where the goal is to account for the misbehavior of a device by identifying the smallest (or most probable) set of components whose failure can explain the observations. 5.2 Compositional Modeling A good component library is carefully constructed to embody a compatible set of modeling ....

R. Davis. Diagnostic reasoning based on structure and behavior. Articial Intelligence, 24:347-410, 1984.

....that rely on models of normal behaviour of the system. Our approach clearly belongs in the family of model based methods that rely on a model of failure behaviour. It has been argued, though, that models of failure behaviour do not perform as well as models of normal behaviour. Davis and Ng [Davis, 1992] [Ng, 1992] for example, claim that a method which relies on a model of normal behaviour can detect and diagnose any fault that will cause a discrepancy between observations of the system and the model predictions. Explicit fault models, on the other hand, are likely to be incomplete or carry ....

Davis R., diagnostic Reasoning Based on Structure and Behaviour, in Hamscher W., Console L., de Kleer J. (eds.), Readings in Model-based Diagnosis, pages 376-407, Morgan Kaufman, 1992, ISBN: 1-55860-249-6.

....anomalies [Davis and Hamscher, 1992] Let us now turn to the second stage of the diagnostic process and examine hypothesis testing. There are two main approaches to this task: constraint suspension and assumption based truth maintenance systems. In constraint suspension (originally described in [Davis, 1984]) the behaviour of each component is modelled as a set of constraints. These constraints define all the possible relationships between the inputs and outputs of the device, or stated more generally, the inferences that can be drawn about the state of one terminal if we know the state of the other ....

Davis R. Diagnostic Reasoning based on Structure and Behaviour, Artificial Intelligence, 24(3): 347-410, Elsevier science, 1984.

....that is, a process of finding reasons, causes and explanations of various events. Introduced by Peirce [5] abduction underlies numerous processes of a great practical importance, such as medical diagnosing [2, 20, 57, 62, 68, 70, 83] testing and repairing technical devices and structures [4, 11, 13, 21, 24, 40, 55, 58, 72], planning a course of action [7, 18, 59] natural language understanding [32, 33, 43, 44, 39, 74, 75] learning [14, 61, 74, 75] Let S denote a Knowledge System (presented in the language of First Order Logic) that describes a part of a real world W . Suppose that a state Obs has been observed ....

Davis, R., 1984, Diagnostic reasoning based on structure and behaviour. Artificial Intelligence 24: 347--410.

....the set of mode assignments that explains the observations. As discussed in [9] in the MBD literature we find two interpretations of the term explain that we have in the definition of diagnosis: explanation as consistency: a diagnosis explains an observation m if it does not contradict m [11], 22] 13] explanation as covering: a diagnosis explains an observation m if it directly supports m. In their unifying proposal, Console and Torasso [9] introduce the definition of an abduction problem with consistency constraints (AP) where they partition the set of observations OBS into two ....

R. Davis "Diagnostic Reasoning based on Structure and Behavior" Artificial Intelligence, vol. 24, pp.347 - 410, 1994.

....of a complete representation of any possible fault [32] We consider only the available fault models and use them in some of our diagnostic activities. Since other activities operate on different knowledge, we do not need a model for any possible fault. In the consistency based approach [12], 15] 27] 33] the diagnostic space (i.e. the space of all the possible diagnoses) is pruned by checking the consistency between the model of the correct functioning of the device and the observed behavior. Advantages and disadvantages of this approach have been already discussed elsewhere ....

R. Davis "Diagnostic reasoning based on structure and behavior" Artificial Intelligence, vol. 24, pp.347 - 410, 1994.

....associations is often employed to denote such knowledge. The classical example of such a system is MYCIN [Shortliffe, 1976] The model based approach to diagnosis has been successfully applied to fault finding in electronic circuits. Early work in this field is described in [Brown et al. 1982] [Davis, 1984], Genesereth, 1984] and [De Kleer, 1976] The study of simple electronic circuits has yielded much insight into the nature of the diagnostic process. More importantly, one of the first formal theories of diagnosis emerged from this research: the theory of consistency based diagnosis as proposed ....

....by findings. If there is a discrepancy between the observed and the predicted behaviour, diagnostic problem solving amounts to isolating the components in the device that are not properly functioning, using a model of the normal structure and behaviour of the device [Brown et al. 1982; Davis, 1984; Davis Hamscher, 1988; Genesereth, 1984; De Kleer, 1976] In doing so, it is assumed that the model of normal structure and behaviour is sufficiently accurate and correct. Figure 2 depicts DNSB diagnosis in a schematic way. DNSB diagnosis is frequently erroneously called model based diagnosis ....

[Article contains additional citation context not shown here]

R. Davis (1984). Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24, 347--410.

....in natural systems arise from differences in process rates (i.e. their time scales) and that these boundaries may not correspond to standard structural decompositions. Even in engineered systems, designed system boundaries cannot be trusted when considering faults or unintended interactions [8]. Reasoning at the level of influences provides more flexibility and overcomes the difficulty of specifying an a priori system decomposition. Additionally, by specifying the criteria for choosing exogenous variables in terms of influence paths, we ensure that the chosen system boundary will be ....

R. Davis. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24:347--410, 1984.

....formally states the conditions it must satisfy before it can be safely plugged into a global system model. The block diagram approach for modeling systems is very closely related to a number of modeling languages that appeared in the context of model based reasoning [de Kleer and Brown 1984, Davis 1984, Genesereth 1984, Kuipers 1994, Franke 1989, Franke and Dvorak 1990] For example, the CC language of 8 Figure 5: A dtool screen shot depicting a system model specified using a block diagram. The figure shows a scenario where the user has set the reports of receivers Y1, Y2 and Y3 to present and ....

Davis, R. 1984. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24:347--410.

....for (see a discussion in [14] it is always informative. Program mutation Program mutation can be used for any kind of symptom. It consists of changing small parts of a program and seeing whether the program still behaves incorrectly [6] This technique is also called constraint suspension [5]. If a mutant does not eliminate the symptom then the parts of the program which have been changed to produce this mutant are assumed correct. A mutant may result from a change in the actual source code or a modification of the behavior of a program component. This technique assumes that the ....

R. Davis. Diagnostic reasoning based on structure and behaviour. Artificial Intelligence, 1-3(24):347--410, December 1984.

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R. Davis. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24:347--410, 1984.

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R. Davis, Diagnostic reasoning based on structure and behavior, Artificial Intelligence 24 (1984) 347--410.

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Randall Davis, `Diagnostic reasoning based on structure and behavior', Artificial Intelligence, 24(1--3), 347--410, (1984).

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Davis R. Diagnostic reasoning based on structure and behavior In Artificial Intelligence, 24 pag 347-410, 1984.

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R. Davis, "Diagnostic reasoning based on structure and behavior," Artif. Intell., vol. 24, pp. 347--410, 1984.

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R. Davis, `Diagnostic reasoning based on structure and behavior', In Artificial Intelligence 24, 347--410, (1984).

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Randall Davis, `Diagnostic reasoning based on structure and behavior', Artificial Intelligence, 24(1--3), 347--410, (1984).

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R. Davis, Diagnostic reasoning based on structure and behavior, Artificial Intelligence 24 (1984) 347--410.

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R. Davis, "Diagnostic reasoning based on structure and behavior," Artif. Intell., vol. 24, pp. 347--410, 1984.

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R. Davis. Diagnostic Reasoning Based on Structure and Behavior. Artificial Intelligence, 24:347-410, 1984.

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R. Davis. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24:347--410, 1984.

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Randall Davis. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24:347--410, 1984.

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R. Davis. Diagnostic reasoning based on structure and behaviour. Artificial Intelligence, 24:347--410, 1984.

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R. Davis, "Diagnostic Reasoning Based on Structure and Behavior," Artificail Inteligence, Vol. 24, pp 347-410, December, 1984.

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. Davis, R., "Diagnostic Reasoning based on Structure and Behaviour", Artificial Intelligence, vol.24, No.1-3, pg.347-410, 1984.

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DAVIS, R. Diagnostic reasoning based on structure and behaviour, Artif. Intell., 1984, 23, 347-410.

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R. Davis, `Diagnostic reasoning based on structure and behavior', Artificial Intelligence, 24(1-3), 347--410, (1984).

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R. Davis: "Diagnostic Reasoning based on Structure and Behavior," Artificial Intelligence 24, 1984

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Davis, R.: Diagnostic reasoning based on structure and behaviour, Artificial Intelligence, 24(1), 347-410, 1984.

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