16 research outputs found

    Task-dependent qualitative domain abstraction

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    AbstractAutomated problem-solving for engineered devices is based on models that capture the essential aspects of the behavior. In this paper, we deal with the problem of automatically abstracting behavior models such that their level of granularity is as coarse as possible, but still sufficiently detailed to carry out a given behavioral prediction or diagnostic task. A task is described by a behavior model, as composed from a library, a specified granularity of the possible observations, and a specified granularity of the desired results. The goal of task-dependent qualitative domain abstraction is to determine maximal partitions for the variables' domains (termed qualitative values) that retain all the necessary distinctions. We present a formalization of this problem within a relational (constraint-based) framework, and devise solutions to automatically determine qualitative values for a device model. The results enhance the ability to use a behavior model of a device as a common basis to support different tasks along its life cycle

    www.elsevier.com/locate/artint Task-dependent qualitative domain abstraction ✩

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    Automated problem-solving for engineered devices is based on models that capture the essential aspects of the behavior. In this paper, we deal with the problem of automatically abstracting behavior models such that their level of granularity is as coarse as possible, but still sufficiently detailed to carry out a given behavioral prediction or diagnostic task. A task is described by a behavior model, as composed from a library, a specified granularity of the possible observations, and a specified granularity of the desired results. The goal of task-dependent qualitative domain abstraction is to determine maximal partitions for the variables ’ domains (termed qualitative values) that retain all the necessary distinctions. We present a formalization of this problem within a relational (constraintbased) framework, and devise solutions to automatically determine qualitative values for a device model. The results enhance the ability to use a behavior model of a device as a common basis to support different tasks along its life cycle

    Constraint Optimization and Abstraction for Embedded Intelligent Systems

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    Task-dependent Qualitative Domain Abstraction ⋆

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    Automated problem-solving for engineered devices is based on models that capture the essential aspects of the behavior. In this paper, we deal with the problem of automatically abstracting behavior models such that their level of granularity is as coarse as possible, but still sufficiently detailed to carry out a given behavioral prediction or diagnostic task. A task is described by a behavior model, as composed from a library, a specified granularity of the possible observations, and a specified granularity of the desired results. The goal of task-dependent qualitative domain abstraction is to determine maximal partitions for the variables ’ domains (termed qualitative values) that retain all the necessary distinctions. We present a formalization of this problem within a relational (constraint-based) framework, and devise solutions to automatically determine qualitative values for a device model. The results enhance the ability to use a behavior model of a device as a common basis to support different tasks along its life cycle. Key words: Model-based systems, Qualitative reasoning, Domain abstraction

    Test Strategy Generation Using Quantified CSPs

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    Using Model Counting to Find Optimal Distinguishing Tests

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    Carlén: Powertrain Diagnostics: A Model-Based Approach

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    team which focuses on the application of model-based techniques to support system analysis tasks and the development of diagnostic solutions. Fulvio Cascio has been with Fiat Reseach Center since 1990. He currently heads the onboard diagnosis group within the software design department
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