Using model checking to help discover mode confusions and other automation surprises

Rushby, John

doi:10.1016/s0951-8320(01)00092-8

Cited by 157 publications

(95 citation statements)

References 7 publications

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“…Automation surprises, difficult decisional compromises between alternatives, or barrier removals are other examples of inconsistency. Automation surprise is a conflict of intention between an automated system and its user (Rushby 2002;Inagaki 2008), which can occur as a result of a number of factors, one of which is the lack of transparency. Relaxing safety constraints can lead to the discovery of new alternative action plans (Ben Yahia et al 2015), or to the discovery of the best compromise between performance criteria (Chen et al 2014).…”

Section: Taxonomy Of Dissonancesmentioning

confidence: 99%

Can dissonance engineering improve risk analysis of human–machine systems?

Vanderhaegen

Carsten

2017

Cogn Tech Work

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The paper discusses dissonance engineering and its application to risk analysis of human-machine systems. Dissonance engineering relates to sciences and technologies relevant to dissonances, defined as conflicts between knowledge. The richness of the concept of dissonance is illustrated by a taxonomy that covers a variety of cognitive and organisational dissonances based on different conflict modes and baselines of their analysis. Knowledge control is discussed and related to strategies for accepting or rejecting dissonances. This acceptability process can be justified by a risk analysis of dissonances which takes into account their positive and negative impacts and several assessment criteria. A risk analysis method is presented and discussed along with practical examples of application. The paper then provides key points to motivate the development of risk analysis methods dedicated to dissonances in order to identify the balance between the positive and negative impacts and to improve the design and use of future human-machine system by reinforcing knowledge.

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Section: Taxonomy Of Dissonancesmentioning

confidence: 99%

Can dissonance engineering improve risk analysis of human–machine systems?

Vanderhaegen

Carsten

2017

Cogn Tech Work

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“…Another approach is to encode assumptions about the user directly into the model (cf. [14]). In this case the separation between device model and user assumptions is not clear and can bias the user assumptions towards those that are needed to make the system work.…”

Section: Discussionmentioning

confidence: 99%

“…Trace resourced = Action resourced * (13) Action resourced = (Action user × PResource) + Action system (14) Hence, for each user action we can include a set of needed resources. This information is not available at the original CTT model, and must be provided in order for the analysis to take place.…”

Section: Tasks and Resourcesmentioning

confidence: 99%

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Supporting Resource-Based Analysis of Task Information Needs

Campos

Doherty

2006

Interactive Systems. Design, Specification, and Verification

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Abstract.We investigate here an approach to modelling the dynamic information requirements of a user performing a number of tasks, addressing both the provision and representation of information, viewing the information as being distributed across a set of resources. From knowledge of available resources at the user interface, and task information needs we can identify whether the system provides the user with adequate support for task execution. We look at how we can use tools to help reason about these issues, and illustrate their use through an example. We also consider a full range of analyses suggested using this approach which could potentially be supported by automated reasoning systems.

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“…John Rushby and his colleagues (Crow, Javaux & Rushby, 2000;Rushby, 2001Rushby, , 2002 analyzed human-automation interaction, demonstrating the use of theorem-provers and model-checkers to explain deviations of pilots' mental models from correct models of autopilot behavior. They showed how formal methods could be used in a cycle of analysis, re-design, and re-analysis to improve a human-machine system.…”

Section: Related Workmentioning

confidence: 99%

Modeling and Formal Analysis of Human-Machine Interaction

Heymann

Degani²,

Shafto

2013

Oxford Handbooks Online

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We describe a formal, model-based, approach for the analysis and design of human-automation interaction. The approach is guided by two primary objectives: first, that the interaction be correct in the sense that it will enable the user to operate the system reliably; second, that the interaction be as simple as possible while maintaining all the required operational flexibility. With these objectives we first discuss a methodology for verification of user-interface correctness, thereby addressing the issue of operational reliability, and then present an algorithmic approach for designing simple and succinct interfaces.

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Using model checking to help discover mode confusions and other automation surprises

Cited by 157 publications

References 7 publications

Can dissonance engineering improve risk analysis of human–machine systems?

Can dissonance engineering improve risk analysis of human–machine systems?

Supporting Resource-Based Analysis of Task Information Needs

Modeling and Formal Analysis of Human-Machine Interaction

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