How to learn from the resilience of Human-Machine Systems?

Authors:
Kiswendsida Abel Ouedraogo;Simon Enjalbert;FréDéRic Vanderhaegen
Affiliations:
Université Lille Nord de France, F-59000 Lille, France and UVHC, LAMIH, F-59313 Valenciennes, France and CNRS, FRE 3304, F-59313 Valenciennes, France;Université Lille Nord de France, F-59000 Lille, France and UVHC, LAMIH, F-59313 Valenciennes, France and CNRS, FRE 3304, F-59313 Valenciennes, France;Université Lille Nord de France, F-59000 Lille, France and UVHC, LAMIH, F-59313 Valenciennes, France and CNRS, FRE 3304, F-59313 Valenciennes, France
Venue:
Engineering Applications of Artificial Intelligence
Year:
2013

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Abstract

This paper proposes a functional architecture to learn from resilience. First, it defines the concept of resilience applied to Human-Machine System (HMS) in terms of safety management for perturbations and proposes some indicators to assess this resilience. Local and global indicators for evaluating human-machine resilience are used for several criteria. A multi-criteria resilience approach is then developed in order to monitor the evolution of local and global resilience. The resilience indicators are the possible inputs of a learning system that is capable of producing several outputs, such as predictions of the possible evolutions of the system's resilience and possible alternatives for human operators to control resilience. Our system has a feedback-feedforward architecture and is capable of learning from the resilience indicators. A practical example is explained in detail to illustrate the feasibility of such prediction.