J. d. Kleer and B. C. Williams. Diagnosing multiple faults. Artificial Intelligence, 32:97--130, 1987.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....D is a diagnosis for S i (1) D [ Sd [ Ctx j V 2Obs abd , 2) D [ Sd [ Ctx [ Obscon 6j . The symbol j denotes the possibly limited reasoning capabilities of a diagnostic agent. I.e: f j j g f j j g. If Obsabd = and Obscon = Obs, then we have a pure consistency based diagnosis [4, 5], and if Obscon = and Obsabd = Obs, we have a pure abductive diagnosis [1] 3. MULTI AGENT DIAGNOSIS In a Multi Agent setting, knowledge of the system S is distributed over the agents, ether semantically and spatially. Combinations are, of course, also possible. The distribution of the ....

J. d. Kleer and B. C. Williams. Diagnosing multiple faults. Arti cial Intelligence, 32:97-130, 1987.

....functions. The key idea underlying model based diagnosis is that a combination of component modes is a possible description of the current state of the spacecraft only if the set of models associated with these modes is consistent with the observed sensor values. Following de Kleer and Williams [9], MI uses a conflict directed best first search to find the most likely combination of component modes consistent with the Helium Fuel Oxidizer inflow = outflow = 0 ###### ##################### #### ##### #### ##### ###### ############################################## ....

J. de Kleer and B. C. Williams, Diagnosing Multiple Faults," Artificial Intelligence, Vol 32, Number 1, 1987.

....Functionality of the ATMS. The ATMS universal propagation algorithm computes the minimal sets of assumptions necessary to derive a given formula. This feature is useful in device diagnosis where one wants to find the minimal number of possible faults that explains a given observed behavior [ de Kleer and Williams, 1987 ] de Kleer and Williams, 1989 ] In fault diagnosis, however, one is often interested in premise sets that contain only a single fault. 3 It should be noted that the ATMS described here is different from the clause management system described in [de Kleer and Reiter, 1987] The clause ....

J. de Kleer and B. Williams. Diagnosing multiple faults. Artificial Intelligence, 32:97-- 130, 1987.

....and on a utility function taking into account breakdown, observations, and repair costs. Upon executing an action and obtaining new observations, the diagnostic reasoner updates the candidate set. Friedrich and Nejdl 1992; Sun and Weld 1992) are representative of this approach and generalize (de Kleer and Williams 1987) dedicated to the choice of the best next measurement. An important limit of these works is their dependence on the assumption that every relevant observation can be made reliably when needed, and on very basic reliable repair actions. Furthermore, the examples used for their illustration are ....

J. de Kleer and B. C. Williams. Diagnosing multiple faults. Artificial Intelligence, 32(1):97--130, 1987.

....it to handle defaults. Examples are Reiter s default logic [Reiter, 1980] Moore s autoepistemic logic [Moore, 1985] McCarthy s circumscription [McCarthy, 1980] In the meantime, truth maintenance systems have been improved. De Kleer developed ATMS [de Kleer, 1986a] and applied it to diagnosis [De Kleer and Williams, 1987], and Goodwin presented an improved TMS [Goodwin, 1987] A first link to our work has been the ATMS extension of Oskar Dressler [Dressler, 1988] Using his nogood inference rule , it became possible to handle negative and disjunctive information by ATMS. However, these developments are not ....

....or cause this malfunctioning. The faults can be broken parts in technical systems or diseases. There are various methods how to find them: The so called consistency based approaches take the normal of the correct behaviour of a system, and use it to predict values for observable attributes (cf. [De Kleer and Williams, 1987], Reiter, 1987] If the predicted values are different to the observed values then contradictions are detected and some parts do not behave as their description. Other approaches use special knowledge about possible faults. In abductive diagnosis, faults are abduced from abnormal observations ....

J. De Kleer and B.C. Williams. Diagnosing multiple faults. Artificial Intelligence, 32:97--130, 1987.

.... prime covers are a way for either exhaustively, or concisely, representing the causes of failure of a system [13, 9] In automated reasoning, a prime cover can be either a set of minimal demonstrations, or the normalization of a formula into conjunctive normal form for further computations [12, 15, 16, 23, 18]. The problem of computing (irredundant, minimal) prime covers of Boolean functions was first addressed by Quine in the 1950 s [21] Since that date, efforts have been done to elaborate efficient prime cover computation procedures [22, 17, 2, 24, 25, 14, 3] For some problems, for instance for ....

J. De Kleer, B. C. Williams, "Diagnosing Multiple Faults", in Artificial Intelligence, Vol 32. pp. 97-- 130, 1987.

....must be utilised. A lot of research has been done on planning techniques with some exciting results [1,3,4,6,13,14,16,17] In this paper, a heuristic Partial Order Planner with Universal quantification s and Conditional effects (UCPOP) is described. Model Based Reasoning (MBR) 2, 5, 7 12,15] has been widely accepted as the principal diagnostic technique in several domains. However, little emphasis has been put on its application to network and system management. In MBR, it is assumed that the structure of a system is known and we can use that knowledge to reason about its ....

J.de Kleer and B.C. Williams, Diagnosing multiple faults, Artificial Intelligence, Vol. 32, No. 1, 97-130, April 1987.

.... such systems as an analogue of Reiter s model based theory of diagnosis [13] We chose Reiter s theory as a point of departure because it provides a formal characterization of diagnosis shared to some extent by most of the model based systems described in the literature, including DART [8] GDE [4] and its descendents [5, 9] and the work of Davis [2] Our approach follows from two basic insights: first, the generality of Reiter s theory of diagnosis makes it applicable to other domains; second, a productive analogy exists between the problem of diagnosis and that of reconfiguration. ....

J. de Kleer and B. C. Williams. Diagnosing multiple faults. Artificial Intelligence, 32(1):97--130, April 1987.

....of computing the prime implicants of a monotone Boolean function in conjunctive normal form. This problem is also investigated in [9] which gives some new results. 6.5. Model based diagnosis. Basic techniques for model based diagnosis have been developed within AI by de Kleer and Williams [13], and by Reiter [45] To introduce the necessary concepts briefly, a system is a pair (SD; COMP ) where SD, the system description, is a set of usually first order sentences and COMP is a set of constants which model the MINIMAL TRANSVERSALS OF A HYPERGRAPH 1301 system components. This general ....

....set of diagnoses for (SD; COMP;OBS) Given C and D for input, deciding if there is an additional diagnosis not contained in D is p m equivalent to co SIMPLE H SAT. The determination of diagnoses from the given minimal conflict sets is essential in popular algorithms for model based diagnosis [45, 13]. Deciding if an already computed set of diagnoses is complete with respect to a given set of minimal conflict sets, i.e. consists of all diagnoses, is an important subproblem if diagnoses are computed incrementally. Therefore, the complexity of the additional diagnosis problem is of crucial ....

J. de Kleer and B. C. Williams, Diagnosing multiple faults, Artificial Intelligence, 32 (1987), pp. 97--130.

.... Ames Research Center (ARC) and the Jet Propulsion Lab (JPL) combines high level planning and scheduling, robust multi threaded execution, and modelbased fault detection isolation and recovery, into an integrated architecture that is able to robustly control a spacecraft over long periods of time [6, 12]. One of the primary components of the Remote Agent architecture is the Livingstone model based health management system. Livingstone is an advanced inference engine that uses a high level declarative model of a physical device to monitor the state of that device, detect off nominal behavior, ....

....off nominal behavior, isolate failures to individual components, and reason about alternative recovery actions. The key benefit provided by Livingstone is the use of a first principles model that describes the behavior of each component within the device and the interactions between the components [6,7,8]. By reasoning generatively about the behavior of the device using the model, Livingstone is able to detect failures whenever a discrepancy occurs between the observations and predictions. In addition, Livingstone is able to use the same model to generate the most likely hypothesis that is ....

J. de Kleer and B. C. Williams, Diagnosing Multiple Faults, Artificial Intelligence, Vol 32, Number 1, 1987.

....Furthermore, our monitoring method is directly applicable to fault diagnosis in dynamic systems. Fault hypotheses can be proposed for monitoring based on initial weak information such as the signs of discrepancies between observations and predictions, by using existing methods such as [ de Kleer and Williams, 1987; Ng, 1991 ] Automatic model building methods can select relevant model fragments from a background knowledge base to express initially weak knowledge about a fault as an SQDE [ Crawford et al. 1990; Rickel and Porter, 1994 ] The observation stream is then used to refine or refute each ....

J. de Kleer and B. C. Williams. Diagnosing Multiple Faults. Artificial Intelligence, 32:97--130, 1987.

....of the rover, MI infers the most likely current state (see Figure 3) MI also provides a layer of abstraction to the executive, allowing plans to be specified in terms of component modes, rather than in terms of low level sensor values. MIR uses algorithms adapted from model based diagnosis [ de Kleer and Williams, 1987; 1989 ] to provide the above functions. MIR extends the basic ideas of modelbased diagnosis by modeling each component as a finite state machine (see Figure 4) and modeling the whole rover as a set of concurrent, synchronous state machines. 4.3 Resource Management Resources on rovers are ....

J. de Kleer and B. C. Williams. Diagnosing multiple faults. Artificial Intelligence, 32:100--117, 1987.

....hypotheses [8, 10] However, they must be given an a priori model of each possible state. One of the strengths of the approach presented in this paper is its ability to construct models from training data and then use them for state identi cation. Qualitative model based diagnosis techniques [2, 6] consider a snapshot of the system rather than its history. In addition, the system state is assumed to be consistent with a propositional description of one of a set of possible states. The presence of noisy data and temporal patterns negates these assumptions. Hidden Markov Models have been ....

J. de Kleer and B. C. Williams. Diagnosing multiple faults. Artif. Intelligence, 32:100-117, 1987.

....25000 30000 10 20 30 40 50 60 70 80 90 100 Number of system nodes in the ROBDD Number of system variables in the domain 2 faults 3 faults 4 faults 5 faults Figure 8: The number of nodes in the layers containing system nodes as a function of the number of system variables. works like [de Kleer and Williams, 1987] and [Williams and Nayak, 1996] For instance, in circuit diagnosis [de Kleer and Williams, 1987] uses a logical model of the system to be diagnosed and determines the next action based on expected Shannon entropy. To calculate the expected Shannon entropy they require the conditional probability ....

....of system variables in the domain 2 faults 3 faults 4 faults 5 faults Figure 8: The number of nodes in the layers containing system nodes as a function of the number of system variables. works like [de Kleer and Williams, 1987] and [Williams and Nayak, 1996] For instance, in circuit diagnosis [de Kleer and Williams, 1987] uses a logical model of the system to be diagnosed and determines the next action based on expected Shannon entropy. To calculate the expected Shannon entropy they require the conditional probability of a set of failed components (termed a candidate in [de Kleer and Williams, 1987] given some ....

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de Kleer, J. and Williams, B. (1987). Diagnosing multiple faults. Articial Intelligence, 32(1):97-130.

....hypotheses [8, 10] However, they must be given an a priori model of each possible state. One of the strengths of the approach presented in this paper is its ability to construct models from training data and then use them for state identification. Qualitative model based diagnosis techniques [2, 6] consider a snapshot of the system rather than its history. In addition, the system state is assumed to be consistent with a propositional description of one of a set of possible states. The presence of noisy data and temporal patterns negates these assumptions. Hidden Markov Models have been ....

J. de Kleer and B. C. Williams. Diagnosing multiple faults. Artif. Intelligence, 32:100--117, 1987.

....In the AI literature on hypothetical reasoning there are relatively few results on the design of experiments for discriminating between competing hypotheses, or on the conclusions one may draw from the outcome of an experiment. There are exceptions, of course. Among these are de Kleer and Williams [4] who provide a probabilistic analysis to decide what measurement to take next. The DART system of Genesereth [5] was capable of proposing circuit inputs and observations to be made in order to confirm or refute a possible diagnosis. TraumAID (Webber et al. 18] is a system for treating trauma ....

....the system under analysis. For example, in the case of circuits, Sigma might describe the individual circuit components, their normal input output behaviour, their fault models, the topology of their interconnections, and the legal combinations of circuit inputs (e.g. deKleer and Williams [4], Reiter [14] We also assume a fixed set HY P of hypotheses. In the case where Sigma describes a circuit, HY P might be the set of diagnoses which we currently hold for this device. How we arrived at the set HY P will be largely irrelevant for our purposes. HY P could be a set of abductive ....

[Article contains additional citation context not shown here]

J. de Kleer and B.C. Williams. Diagnosing multiple faults. Artificial Intelligence, 32:97--130, 1987.

....branches of work for state estimation and fault diagnosis are Kalman filters from control theory and qualitative model based diagnosis from artificial intelligence. State estimation for rovers exceeds the capabilities of the current approaches. Qualitative model based techniques for diagnosis [2, 6] rely on the system transitioning occasionally from one steady state to another. Rovers receive rapidlychanging streams of continuous valued sensor data. In addition, the model based techniques often rely on a snapshot of the system, disregarding history. But in fact the history may be critical to ....

J. de Kleer and B. C. Williams. Diagnosing multiple faults. Artif. Intelligence, 32:100--117, 1987.

....on hypothetical reasoning there are relatively few results on the design of tests for discriminating a space of hypotheses, or on the conclusions one may draw from the outcome of a test. There are exceptions of course, particularly in the area of diagnosis. Among these are de Kleer and Williams [ de Kleer and Williams, 1987 ] who provide a probabilistic analysis to decide what measurement to take next. The DART system of Genesereth [ Genesereth, 1984 ] was capable of proposing circuit inputs and observations to be made in order to confirm or refute a possible diagnosis. TraumAID [ Webber et al. 1990 ] is a system ....

....knowledge describing the system under analysis. For example, in the case of circuits, Sigma might describe the individual circuit components, their normal input output behaviour, their fault models, the topology of their interconnections, and the legal combinations of circuit inputs (e.g. de Kleer and Williams, 1987 ] Reiter, 1987 ] We also assume a fixed set HY P of hypotheses. In the case where Sigma describes a circuit, HY P might be the set of diagnoses which we currently hold for this device. How we arrived at the set HY P will be largely irrelevant for our purposes. HY P could be a set of ....

[Article contains additional citation context not shown here]

J. de Kleer and B.C. Williams. Diagnosing multiple faults. Artificial Intelligence, 32:97--130, 1987.

....the batteries. Either way, my flashlight is now emitting a light, which was my ultimate goal. Traditionally, the AI research on diagnosis has focused on the problem of determining a set of candidate diagnoses, given a description of system behavior and an observation of aberrant behavior (e.g. (de Kleer and Williams, 1987), Reiter, 1987) Testing could subsequently be performed to acquire sufficient discriminatory observations in order to identify a unique diagnosis. Recently, some researchers have cast diagnostic problem solving in a more purposive role (e.g. Provan and Poole, 1991) Friedrich and Nejdl, ....

J. de Kleer and B. Williams (1987). Diagnosing multiple faults. In Artificial Intelligence Journal 32:97--130.

....rather than in terms of low level sensor values. MR supports the run time generation of novel reconfiguration actions to return components to the desired mode or to re enable high level capabilities such as able to produce thrust . Livingstone uses algorithms adapted from model based diagnosis [11, 12] to provide the above functions. The key idea underlying model based diagnosis is that a combination of component modes is a possible description of the current state of the spacecraft only if the set of models associated with these modes is consistent with the observed sensor values. Following de ....

J. de Kleer and B. C. Williams, Diagnosing Multiple Faults, Artificial Intelligence, Vol 32, Number 1, 1987.

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J. d. Kleer and B. C. Williams. Diagnosing multiple faults. Artificial Intelligence, 32:97--130, 1987.

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J. Kleer and B. C. Williams. Diagnosing multiple faults. Artificial Intelligence, 32(1):97--130, 1987.

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J. d. Kleer and B. C. Williams. Diagnosing multiple faults. Artificial Intelligence, 32:97--130, 1987.

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J. de Kleer, B. Williams, Diagnosing Multiple Faults, Artificial Intelligence, 1987

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Intelligence 115:145--214. de Kleer, J., and Williams, B. 1987. Diagnosing multiple faults. Artificial Intelligence 32:97--130.

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