Development of an Emotion-Competent SLAM Agent

Abstract

Emotions are a fundamental part of everyday life and an important topic in the development of artificial intelligence. We combine a Simultaneous Localization and Mapping algorithm with a model of emotion. The model of emotion is able to generate a mapping from the quantitative figures of the SLAM process to human-like emotions. This enables the robot to communicate its current state towards a human observer using emotional expressions. The paper reports on the design of the model, the result of the affective evaluation during an autonomous path finding process and its comparison to experimental data of a survey.