Elements of artificial emotion
T. Gomi, J. Vardalas, Koh-Ichi Ide · 2002
Recent implementations of action selection dynamics (ASD) with learning in situated/embodied form, increased the potential for more timely, dynamic, and vigorous interactions between the autonomous agent and its environment than Maes' (1989) simulation of ASD demonstrated and implied. The most recent implementations of ASD is an attempt to create a framework in which the Urge Theory of M. Toda can be investigated. It produced improvements in implementational efficiency and theoretical accuracy of ASD. The ASD network gets inputs from several different sensors (including vision), and supports learning to change inter-agent network relationships. Emotional states such as fear, curiosity, affection-seeking, hunger, joy, irritation, and anger are supported as emergent phenomena. The robot's on-board voice synthesis unit announces its internal states.