Evolutionary algorithm for a genetic robot’s personality based on the Myers–Briggs Type Indicator

The genetic robot has many configurable genes that contribute to defining the robot’s personality. The large number of genes allows for a highly complex system, however it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the robot’s personality while manually initializing values for the individual genes. To overcome this difficulty, this paper proposes MBTI-EAGRP. It is a fully autonomic gene-generative algorithm for a genetic robot’s personality in a mobile phone. After grasping the user preferences through MBTI assessment using the neural network algorithm, the evolutionary algorithm generates and evolves a gene pool that customizes the robot’s genome so that it closely matches a simplified set of personality features preferred by the user. Finally, an evaluation procedure for individuals is carried out in a virtual environment using tailored perception scenarios and real MBTI measurements.

► An MBTI-based-Evolutionary Algorithm for a Genetic Robot’s Personality is proposed.
► The Self-Organizing feature Map establishes a user preference through the MBTI.
► MBTI-EAGRP characterizes a variety of internal states and their concomitant behaviors.
► MBTI-EAGRP is demonstrated by a software robot in a mobile phone and a real robot.