29 research outputs found

    On the construction of hierarchic models

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    One of the main problems in the field of model-based diagnosis of technical systems today is finding the most useful model or models of the system being diagnosed. Often, a model showing the physical components and the connections between them is all that is available. As systems grow larger and larger, the run-time performance of diagnostic algorithms decreases considerably when using these detailed models. A solution to this problem is using a hierarchic model. This allows us to first diagnose the system using an abstract model, and then use this solution to guide the diagnostic process using a more detailed model. The main problem with this approach is acquiring the hierarchic model. We give a generic hierarchic diagnostic algorithm and show how the use of certain classes of hierarchic models can increase the performance of this algorithm. We then present linear time algorithms for the automatic construction of these hierarchic models, using the detailed model and extra information about cost of probing points and invertibility of components

    Redesign of technical systems

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    The paper describes a systematic approach to support the redesign process. Redesign is the adaptation of a technical system in order to meet new specifications. The approach presented is based on techniques developed in model-based diagnosis research. The essence of the approach is to find the part of the system which causes the discrepancy between a formal specification of the system to be designed and the description of the existing technical system. Furthermore, new specifications are generated, describing the new behaviour for the `faulty¿ part. These specifications guide the actual design of this part. Both the specification and design description are based on YMIR, an ontology for structuring engineering design knowledge

    Improving performance through concept formation and conceptual clustering

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    Research from June 1989 through October 1992 focussed on concept formation, clustering, and supervised learning for purposes of improving the efficiency of problem-solving, planning, and diagnosis. These projects resulted in two dissertations on clustering, explanation-based learning, and means-ends planning, and publications in conferences and workshops, several book chapters, and journals; a complete Bibliography of NASA Ames supported publications is included. The following topics are studied: clustering of explanations and problem-solving experiences; clustering and means-end planning; and diagnosis of space shuttle and space station operating modes

    Do We Really Sample Right In Model-Based Diagnosis?

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    Statistical samples, in order to be representative, have to be drawn from a population in a random and unbiased way. Nevertheless, it is common practice in the field of model-based diagnosis to make estimations from (biased) best-first samples. One example is the computation of a few most probable possible fault explanations for a defective system and the use of these to assess which aspect of the system, if measured, would bring the highest information gain. In this work, we scrutinize whether these statistically not well-founded conventions, that both diagnosis researchers and practitioners have adhered to for decades, are indeed reasonable. To this end, we empirically analyze various sampling methods that generate fault explanations. We study the representativeness of the produced samples in terms of their estimations about fault explanations and how well they guide diagnostic decisions, and we investigate the impact of sample size, the optimal trade-off between sampling efficiency and effectivity, and how approximate sampling techniques compare to exact ones

    Real-time value-driven diagnosis

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    Diagnosis is often thought of as an isolated task in theoretical reasoning (reasoning with the goal of updating our beliefs about the world). We present a decision-theoretic interpretation of diagnosis as a task in practical reasoning (reasoning with the goal of acting in the world), and sketch components of our approach to this task. These components include an abstract problem description, a decision-theoretic model of the basic task, a set of inference methods suitable for evaluating the decision representation in real-time, and a control architecture to provide the needed continuing coordination between the agent and its environment. A principal contribution of this work is the representation and inference methods we have developed, which extend previously available probabilistic inference methods and narrow, somewhat, the gap between probabilistic and logical models of diagnosis
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