An enhanced architecture for OFMspert: a domain-independent system for intent inferencing

Thurman, David A.; Chappell, Alan R.; Mitchell, Christine

doi:10.1109/icsmc.1998.725539

Cited by 27 publications

(10 citation statements)

References 11 publications

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“…Human task models were created using an intermediary language called enhanced operator function model (EOFM) [4], an XML-based, generic human task modeling language based on the operator function model (OFM) [21,26]. EOFMs are hierarchical and heterarchical representations of goal or mission driven activities that decompose into lower level activities, and finally, atomic actions—where actions can represent any observable, cognitive, or perceptual human behavior.…”

Section: Methodsmentioning

confidence: 99%

Formally verifying human–automation interaction as part of a system model: limitations and tradeoffs

Bolton

Bass

2010

Innovations Syst Softw Eng

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Both the human factors engineering (HFE) and formal methods communities are concerned with improving the design of safety-critical systems. This work discusses a modeling effort that leveraged methods from both fields to perform formal verification of human–automation interaction with a programmable device. This effort utilizes a system architecture composed of independent models of the human mission, human task behavior, human-device interface, device automation, and operational environment. The goals of this architecture were to allow HFE practitioners to perform formal verifications of realistic systems that depend on human–automation interaction in a reasonable amount of time using representative models, intuitive modeling constructs, and decoupled models of system components that could be easily changed to support multiple analyses. This framework was instantiated using a patient controlled analgesia pump in a two phased process where models in each phase were verified using a common set of specifications. The first phase focused on the mission, human-device interface, and device automation; and included a simple, unconstrained human task behavior model. The second phase replaced the unconstrained task model with one representing normative pump programming behavior. Because models produced in the first phase were too large for the model checker to verify, a number of model revisions were undertaken that affected the goals of the effort. While the use of human task behavior models in the second phase helped mitigate model complexity, verification time increased. Additional modeling tools and technological developments are necessary for model checking to become a more usable technique for HFE.

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Section: Methodsmentioning

confidence: 99%

Formally verifying human–automation interaction as part of a system model: limitations and tradeoffs

Bolton

Bass

2010

Innovations Syst Softw Eng

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“…Human task models are created using an intermediary language called enhanced operator function model (EOFM) [45], an XMLbased human task modeling language derived from the operator function model [56], [63]. EOFMs are hierarchical and heterarchical representations of goal-driven activities that decompose into lower level activities, and finally, atomic actions.…”

Section: Eofm and The Formal Verification Of Haimentioning

confidence: 99%

Generating Erroneous Human Behavior From Strategic Knowledge in Task Models and Evaluating Its Impact on System Safety With Model Checking

Bolton

Bass

2013

IEEE Trans. Syst. Man Cybern, Syst.

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Human-automation interaction, including erroneous human behavior, is a factor in the failure of complex, safety-critical systems. This paper presents a method for automatically generating formal task analytic models encompassing both erroneous and normative human behavior from normative task models, where the misapplication of strategic knowledge is used to generate erroneous behavior. Resulting models can be automatically incorporated into larger formal system models so that safety properties can be formally verified with a model checker. This allows analysts to prove that a human-automation interactive system (as represented by the formal model) will or will not satisfy safety properties with both normative and generated erroneous human behavior. Benchmarks are reported that illustrate how this method scales. The method is then illustrated with a case study: the programming of a patientcontrolled analgesia pump. In this example, a problem resulting from a generated erroneous human behavior is discovered. The method is further employed to evaluate the effectiveness of different solutions to the discovered problem. The results and future research directions are discussed.Index Terms-Formal methods, human error, humanautomation interaction (HAI), model checking, system safety, task analysis.

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“…goals), human task behavior (the activities and actions the human operator uses to achieve mission goals), human-device interfaces (displays and controls available to the human operator through the device), device automation (underlying device behavior), and the operational environment (Bolton and Bass, 2010a). In our analysis framework, human task models are created using an intermediary language called Enhanced Operator Function Model (EOFM) (Bolton et al, 2011), an XML-based human task modeling representation derived from the Operator Function Model (OFM) (Mitchell and Miller, 1986; Thurman et al, 1998). EOFMs are hierarchical representations of goal driven activities that decompose into lower level activities, and finally, atomic actions.…”

Section: Using Eofm In the Formal Verification Of Haimentioning

confidence: 99%

Generating phenotypical erroneous human behavior to evaluate human–automation interaction using model checking

Bolton¹,

Bass²,

Siminiceanu³

2012

International Journal of Human-Computer Studies

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Breakdowns in complex systems often occur as a result of system elements interacting in unanticipated ways. In systems with human operators, human-automation interaction associated with both normative and erroneous human behavior can contribute to such failures. Model-driven design and analysis techniques provide engineers with formal methods tools and techniques capable of evaluating how human behavior can contribute to system failures. This paper presents a novel method for automatically generating task analytic models encompassing both normative and erroneous human behavior from normative task models. The generated erroneous behavior is capable of replicating Hollnagel’s zero-order phenotypes of erroneous action for omissions, jumps, repetitions, and intrusions. Multiple phenotypical acts can occur in sequence, thus allowing for the generation of higher order phenotypes. The task behavior model pattern capable of generating erroneous behavior can be integrated into a formal system model so that system safety properties can be formally verified with a model checker. This allows analysts to prove that a human-automation interactive system (as represented by the model) will or will not satisfy safety properties with both normative and generated erroneous human behavior. We present benchmarks related to the size of the statespace and verification time of models to show how the erroneous human behavior generation process scales. We demonstrate the method with a case study: the operation of a radiation therapy machine. A potential problem resulting from a generated erroneous human action is discovered. A design intervention is presented which prevents this problem from occurring. We discuss how our method could be used to evaluate larger applications and recommend future paths of development.

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An enhanced architecture for OFMspert: a domain-independent system for intent inferencing

Cited by 27 publications

References 11 publications

Formally verifying human–automation interaction as part of a system model: limitations and tradeoffs

Formally verifying human–automation interaction as part of a system model: limitations and tradeoffs

Generating Erroneous Human Behavior From Strategic Knowledge in Task Models and Evaluating Its Impact on System Safety With Model Checking

Generating phenotypical erroneous human behavior to evaluate human–automation interaction using model checking

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