A reinforced iterative formalism to learn from human errors and uncertainty
| Autor: | Stéphane Zieba, Philippe Polet, Frédéric Vanderhaegen |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Formalism (philosophy of mathematics)
Mathematical optimization Artificial neural network Artificial Intelligence Control and Systems Engineering Computer science business.industry Human error Iterative learning control Artificial intelligence Electrical and Electronic Engineering business Reinforcement |
| Zdroj: | Engineering Applications of Artificial Intelligence. 22:654-659 |
| ISSN: | 0952-1976 |
| DOI: | 10.1016/j.engappai.2009.01.008 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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