An Algorithm for Self-Motivated Hierarchical Sequence Learning

This work demonstrates a mechanism that autonomously organizes an agent’s sequential behavior. The behavior organization is driven by pre-defined values associated with primitive behavioral patterns. The agent learns increasingly elaborated behaviors through its interactions with its environment. These learned behaviors are gradually organized in a hierarchy that reflects how the agent exploits the hierarchical regularities afforded by the environment. To an observer, the agent thus appears to exhibit basic selfmotivated, sensible, and learning behavior to fulfill its inborn predilections. As such, this work illustrates Piaget’s theories of early-stage developmental learning.