Iterative learning control based tools to learn from human error

This paper proposes a new alternative to identify and predict intentional human errors based on benefits, costs and deficits (BCD) associated to particular human deviations. It is based on an iterative learning system. Two approaches are proposed. These approaches consist in predicting barrier removal, i.e., non-respect of rules, achieved by human operators and in using the developed iterative learning system to learn from barrier removal behaviours. The first approach reinforces the parameters of a utility function associated to the respect of this rule. This reinforcement affects directly the output of the predictive tool. The second approach reinforces the knowledge of the learning tool stored into its database. Data from an experimental study related to driving situation in car simulator have been used for both tools in order to predict the behaviour of drivers. The two predictive tools make predictions from subjective data coming from drivers. These subjective data concern the subjective evaluation of BCD related to the respect of the right priority rule.