Learning of Behavior Trees for Autonomous Agents

Abstract

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. In this paper, we propose a model-free AP framework using Genetic Programming (GP) to derive an optimal BT for an autonomous agent to achieve a given goal in unknown (but fully observable) environments. We illustrate the proposed framework using experiments conducted with an open source benchmark Mario AI for automated generation of BTs that can play the game character Mario to complete a certain level at various levels of difficulty to include enemies and obstacles.

Cited by in corpus (5)

• Behavior Trees and State Machines in Robotics Applications
• Behavior Trees in Robot Control Systems
• Deliberative Acting, Online Planning and Learning with Hierarchical Operational Models
• A Stack-of-Tasks Approach Combined with Behavior Trees: a New Framework for Robot Control
• Verifying Safety of Behaviour Trees in Event-B