Example of a Complementary Use of Model Checking and Human Performance Simulation

Gelman, Gabriel; Feigh, Karen M.; Rushby, John

doi:10.1109/thms.2014.2331034

Cited by 17 publications

(9 citation statements)

References 31 publications

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“…In [14] the PVS theorem prover and the NuSMV model checker are used to find the potential source of automation surprises in a flight guidance system. In contrast to our work, the work in [13,14] does not deal with multitasking, and [14] does not focus on the cognitive aspects of human behavior.…”

Section: Example: Interacting With a Gps Device While Drivingmentioning

confidence: 86%

“…On the formal methods side, Gelman et al [13] model a pilot and the flight management system (FMS) of a civil aircraft and use WMC simulation and SAL model checking to study automation suprises (i.e., the system works as designed but the pilot is unable to predict or explain the behavior of the aircraft). In [14] the PVS theorem prover and the NuSMV model checker are used to find the potential source of automation surprises in a flight guidance system.…”

Section: Example: Interacting With a Gps Device While Drivingmentioning

confidence: 99%

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An Executable Formal Framework for Safety-Critical Human Multitasking

Broccia

Milazzo

Ölveczky

2018

Lecture Notes in Computer Science

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When a person is concurrently interacting with different systems, the amount of cognitive resources required (cognitive load) could be too high and might prevent some tasks from being completed. When such human multitasking involves safety-critical tasks, for example in an airplane, a spacecraft, or a car, failure to devote sufficient attention to the different tasks could have serious consequences. To study this problem, we define an executable formal model of human attention and multitasking in Real-Time Maude. It includes a description of the human working memory and the cognitive processes involved in the interaction with a device. Our framework enables us to analyze human multitasking through simulation, reachability analysis, and LTL and timed CTL model checking, and we show how a number of prototypical multitasking problems can be analyzed in Real-Time Maude. We illustrate our modeling and analysis framework by studying the interaction with a GPS navigation system while driving, and apply model checking to show that in some cases the cognitive load of the navigation system could cause the driver to keep the focus away from driving for too long.

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Section: Example: Interacting With a Gps Device While Drivingmentioning

confidence: 86%

Section: Example: Interacting With a Gps Device While Drivingmentioning

confidence: 99%

An Executable Formal Framework for Safety-Critical Human Multitasking

Broccia

Milazzo

Ölveczky

2018

Lecture Notes in Computer Science

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“…Concerning automation surprises, and in particular mode confusion, Rushby et al [24], [25] and Bredereke and Lankenau [26], [27] have worked on formalizing mode confusion and have proposed techniques to reduce it. The former used the Murφ model-checker, while the latter worked on specification/implementation refinement relations.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Potential Automation Surprises for ADEPT Models

Combéfis¹,

Giannakopoulou

Pecheur

2016

IEEE Trans. Human-Mach. Syst.

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This paper describes how to automatically detect potential automation surprises in interactive systems, within a rapid automation interface design tool named ADEPT. The proposed analysis method in this paper is based on a conformance relation, called full-control, between the model of the actual system and a mental model of it, that is, its behavior as perceived by the operator. The method can, among other things, automatically generate a so-called minimal full-control mental model for a given system. Systems are well designed if they can be described by relatively simple mental models for their operators, which can be assessed with the minimal full-control mental model generation algorithms. During the generation, potential automation surprises are detected and highlighted with execution examples that may lead to confusion. The analysis methods are based on an enriched version of labeled transition systems to describe the system and mental models. In order to be able to integrate the analysis method within ADEPT, a semantics for ADEPT models makes it possible to translate them into enriched LTSs. The proposed translation is automated for a specified class of ADEPT models that are characterized and defined in this paper. A case study demonstrates the proposed analysis framework and informs how the integration with ADEPT can be improved.Index Terms-ADEPT toolset, formal methods, human factors, human-machine interaction.

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“…Model checking is an important area of interest due to its relevant social impact in improving the reliability of complex systems. A few recent examples include: the application of model checking to aircraft automation to help uncover problematic human-automation interaction [11]; the automatic generation of properties from task models for detecting human-automation surprises [12]; the automatic characterization of human activities in temporal logic that provide properties for the verification of ambient-assisted living applications [13], and an approach to help improve user manuals for medical devices [2]. These represent a small sampling of the social relevance of model checking in human-machine interaction.…”

Section: A Model Checkingmentioning

confidence: 99%

Guest Editorial Special Issue on Systematic Approaches to Human–Machine Interface: Improving Resilience, Robustness, and Stability

Mercer

Rungta

Gillan

2016

IEEE Trans. Human-Mach. Syst.

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W E are pleased to present the special issue on "Systematic Approaches to Human-Machine Interface: Improving Resilience, Robustness, and Stability." The motivation for this special issue is the growing increase of remote mission management, unmanned aircraft systems, NextGen operations in the U.S. and its Single European Sky Air Traffic Management Research counterparts in Europe, and other similarly integrated systems of systems that include complex human-machine systems with high levels of autonomy and team dynamics that are difficult to understand and analyze. It is important to develop techniques that facilitate a better characterization of the behaviors and performance of these complex systems, first in the lab and, then, in the field. Techniques including formal verification, cognitive modeling, and task analysis are being explored in several disciplines [1], [2]. These efforts strive to quantify resilience, robustness, stability, and other properties of these complex systems. This special issue presents papers which epitomize interdisciplinary works that cross the various communities studying these important and complex human-machine interactions. The issue explores key research areas that impact the properties of these systems, which rely on varied degrees of human and machine interactions. The special issue is a result of the continued interest in the formal verification of complex human-machine systems.

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Example of a Complementary Use of Model Checking and Human Performance Simulation

Cited by 17 publications

References 31 publications

An Executable Formal Framework for Safety-Critical Human Multitasking

An Executable Formal Framework for Safety-Critical Human Multitasking

Automatic Detection of Potential Automation Surprises for ADEPT Models

Guest Editorial Special Issue on Systematic Approaches to Human–Machine Interface: Improving Resilience, Robustness, and Stability

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