Learning of Behavior Trees for Autonomous Agents

Understand

Definition of an accurate system model for Automated Planner (AP) is often impractical, especially for real-world problems.

• Conversely, off-the-shelf planners fail to scale up and are domain dependent.
• These drawbacks are inherited from conventional transition systems such as Finite State Machines (FSMs) that describes the action-plan execution generated by the AP.
• On the other hand, Behavior Trees (BTs) represent a valid alternative to FSMs presenting many advantages in terms of modularity, reactiveness, scalability and domain-independence.