Temporal fuzzy based modeling as applied to the class of man-machine interaction

Cognitive robotics have been recently touted as effective tools that could be used in a number of applications. In order for cognitive robots to act adequately and safely in real world, they must be able to achieve effective human-machine interaction or collaboration. Toward this end, performance evaluation metrics are used as important measures to achieve these goals. Toward the efficient modelling of such metrics, we attempt to determine the true time that an operator has to dedicate to the robot. Therefore, we define the robot attention demand (RAD) as a function of both Direct Interaction Time (DIT) and Indirect Interaction Time (IIT), where the IIT is a direct consequence of human trust in automation. We then propose a two-level fuzzy temporal model to evaluate and estimate the human trust in automation while collaborating and interacting with robots and machines to complete some tasks. The model combines the advantages of fuzzy logic and finite state machines to best model this phenomenon, and reduces the system complexity and the size of the knowledge base by grouping perception into first- and second-order perceptions.