Analysis and comparison of iterative learning control schemes
Engineering Applications of Artificial Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-organizing maps for learning the edit costs in graph matching
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Technical Communique: Resilient linear filtering of uncertain systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Technical communique: A note on causal and CITE iterative learning control algorithms
Automatica (Journal of IFAC)
Information Sciences: an International Journal
A multi-viewpoint system to support abductive reasoning
Information Sciences: an International Journal
Iterative learning control based tools to learn from human error
Engineering Applications of Artificial Intelligence
How to learn from the resilience of Human-Machine Systems?
Engineering Applications of Artificial Intelligence
Using the BCD model for risk analysis: An influence diagram based approach
Engineering Applications of Artificial Intelligence
An evidential network approach to support uncertain multiviewpoint abductive reasoning
Information Sciences: an International Journal
Hi-index | 0.00 |
This paper proposes a reinforced iterative formalism to learn from intentional human errors called barrier removal and from uncertainty on human-error parameters. Barrier removal consists in misusing a safety barrier that human operators are supposed to respect. The iterative learning formalism is based on human action formalism that interprets the barrier removal in terms of consequences, i.e. benefits, costs and potential dangers or deficits. Two functions are required: the similarity function to search a known case closed to the input case for which the human action has to be predicted and a reinforcement function to reinforce links between similar known cases. This reinforced iterative formalism is applied to a railway simulation from which the prediction of barrier removal is based on subjective data.