Fuzzy Knowledge-Based Architecture for Learning and Interaction in Social Robots
This work addresses improving interaction flexibility and understandability in social robots for healthcare, but it is incremental as it builds on prior models.
The authors extended a cognitive-based emotion model for social robots by embedding a fuzzy rule-based system to regulate behavior and decision-making in interactions, implementing it on a healthcare robot and showing it interacts reasonably with patients under defined conditions.
In this paper, we introduce an extension of our presented cognitive-based emotion model [27][28]and [30], where we enhance our knowledge-based emotion unit of the architecture by embedding a fuzzy rule-based system to it. The model utilizes the cognitive parameters dependency and their corresponding weights to regulate the robot's behavior and fuse their behavior data to achieve the final decision in their interaction with the environment. Using this fuzzy system, our previous model can simulate linguistic parameters for better controlling and generating understandable and flexible behaviors in the robots. We implement our model on an assistive healthcare robot, named Robot Nurse Assistant (RNA) and test it with human subjects. Our model records all the emotion states and essential information based on its predefined rules and learning system. Our results show that our robot interacts with patients in a reasonable, faithful way in special conditions which are defined by rules. This work has the potential to provide better on-demand service for clinical experts to monitor the patients' emotion states and help them make better decisions accordingly.