Representing Causality and Reasoning about Controllability of Multi-level Flow-Systems

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    Abstract

    Safe operation of complex processes requires that operators maintain situational-awareness even in highly automated environments. Automatic reasoning can support operators as well as the automation system itself to react effectively and appropriately to disturbances. However, knowledge-based reasoning about control situations remains a challenge due to the entanglement of process and control systems that co-establish the intended causal structure of a process. Due to this entanglement, reasoning about such systems depends on a coherent representation of control and process. This paper explains modeling of controlled processes with multilevelflow models and proposes a new framework for modeling causal influence in multilevel flow models on the basis of a flow/potential analogy. The results are illustrated on examples from the domain of electric power systems.
    Original languageEnglish
    Title of host publication2010 IEEE International Conference on Systems, Man, and Cybernetics : Intelligent Systems for a Safe and Secure World
    PublisherIEEE
    Publication date2010
    ISBN (Print)978-1-4244-6586-6
    DOIs
    Publication statusPublished - 2010
    Event2010 IEEE International Conference on Systems, Man and Cybernetics - Istanbul, Turkey
    Duration: 10 Oct 201013 Oct 2010
    https://ieeexplore.ieee.org/xpl/conhome/5629466/proceeding

    Conference

    Conference2010 IEEE International Conference on Systems, Man and Cybernetics
    Country/TerritoryTurkey
    CityIstanbul
    Period10/10/201013/10/2010
    Internet address

    Bibliographical note

    Copyright 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Keywords

    • Causality
    • Knowledge-based Systems
    • Functional Modeling
    • Model-based reasoning
    • Power Systems
    • Intelligent Control
    • Means-ends

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