Abstract
This paper investigates how the personality and attitudes of intelligent agents could be designed to most effectively promote bonding. Observational data are collected from a series of conversations, and a measure of bonding is adapted and verified. The effects of personality and dispositional attitudes on bonding are analyzed, and we find that attentiveness and excitement are more effective at promoting bonding than traits like attractiveness and humour.
Type
Publication
In Intelligent Virtual Agents (IVA)
Natasha Jaques
My research is focused on Social Reinforcement Learning–developing algorithms that use insights from social learning to improve AI agents’ learning, generalization, coordination, and human-AI interaction.