Fuzzy Knowledge-Based Architecture for Learning and Interaction in Social Robots

TL;DR

This paper presents a fuzzy knowledge-based architecture for social robots that enhances emotional modeling and interaction, demonstrated on a healthcare robot interacting with patients to improve emotional understanding and decision-making.

Contribution

The paper introduces a fuzzy rule-based extension to a cognitive emotion model, enabling more flexible and understandable robot behaviors in social interactions.

Findings

Robot interacts reasonably with patients in predefined conditions

Fuzzy system improves emotion regulation and behavior generation

Model records emotion states and supports decision-making

Abstract

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.