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

Kiswendsida Abel Ouedraogo (1) , Simon Enjalbert (1) , Frédéric Vanderhaegen (1)

1 LAMIH - Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201

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.

Mots clés: "Feedback/feedforward control"; Human-Machine Systems; Learning process; Resilience

Domaines: Sciences de l'ingénieur [physics]; Automatique / Robotique

Kiswendsida Abel Ouedraogo, Simon Enjalbert, Frédéric Vanderhaegen. How to learn from the resilience of Human–Machine Systems?. Engineering Applications of Artificial Intelligence, 2013, 26 (1), pp.24-34. ⟨10.1016/j.engappai.2012.03.007⟩. ⟨hal-03510010⟩