Robotic Dancing: Exploring Agents that use a Stratified Perceive-Decide-Act Cycle of Interaction

An autonomous agent is any intelligent entity that engages in a perceive-decide-act cycle of interaction with its environment. In this paper we present a formalism using a stratified percept chain that renders an augmented interactive cycle. In particular, we identify two kinds of agents: a “lead” agent, who gains the percepts directly from the environment, and a “follow” agent, who gains its percepts from the lead agent (as well as the environment). In this work, the lead agent procures percepts from the environment, makes decisions based on this input, and passes these new orders in the form of secondary percepts onto the “follow”. We exemplify this formalism in the application area of robotic dancing using models programmed in Alice, a programming IDE/language that facilitates the creation and visualization of autonomous agents.