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Tasks Decomposition of System Models for Human-Machine Interaction Analysis

Authors
Guillaume Maudoux, Sébastien Combéfis, Charles Pecheur
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Title
Tasks Decomposition of System Models for Human-Machine Interaction Analysis
Authors
Guillaume Maudoux, Sébastien Combéfis, Charles Pecheur
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Type
In Proceedings
Book title
Proceedings of the Workshop on Formal Methods for Human Computer Interaction 2015
Pages
7-12
Editor
Benjamin Weyers, Judy Bowen, Alan Dix, Philippe Palanque
Year
2015

Abstract

This paper is concerned with the problem of learning how to interact safely with complex automated systems. With large systems, human-machine interaction errors like automation surprises are more likely to happen. Previous works have introduced the notion of full-control mental models for op- erators. These are formal system abstractions embedding the required information to control a system completely and without surprises. Full-control mental models can be used as training material but are ineffective as their control over a system is only guaranteed when fully learned.

This work investigates the problem of decomposing full- control mental models into smaller independent tasks. These tasks each allow to control a subset of the system and can be learned incrementally to control more and more features of the system. This paper proposes an operator that describes how two mental models are merged when learned sequen- tially. With that operator, we show how to generate a set of small tasks with the required properties.

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BibTeX Record
  @INPROCEEDINGS{lvl-2015-885814,
    TITLE = {Tasks Decomposition of System Models for Human-Machine Interaction Analysis},
    AUTHOR = {Guillaume Maudoux and Sébastien Combéfis and Charles Pecheur},
    YEAR = {2015},
    PAGES =  {7-12},
    EDITOR = {Benjamin Weyers and Judy Bowen and Alan Dix and Philippe Palanque},
    URL = {https://lvl.info.ucl.ac.be/Publications/TasksDecompositionOfSystemModelsForHuman-MachineInteractionAnalysis},
  }