Overview
Agent behavior in games and simulation is often implemented with behavior trees (BT) or finite-state machines (FSM), but as complexity grows the logic becomes hard to maintain and explain. HTN (Hierarchical Task Network) decomposes high-level goals layer by layer into executable atomic tasks, making AI decision-making structured, explainable, and reusable.
DawnEngine ships an HTN planner, well suited to simulation and advanced NPCs that need complex, auditable behavior.
Key Capabilities
- Hierarchical decomposition: plan from abstract goals down to concrete actions.
- Explainable decisions: every decision has a clear decomposition chain for debugging and auditing.
- Domain reuse: methods and operators are reusable across scenarios.
- Dynamic replanning: replan when the environment changes to keep behavior sensible.
- Scale: supports concurrent planning for large numbers of agents.
Use Cases
- Military / emergency / traffic simulation that needs explainable decisions.
- Advanced NPCs and cooperative agents driven by complex tasks.
- Projects that want to capture domain knowledge as a reusable planning library.
Related Features
HTN planning often pairs with motion: 3D flight navigation provides aerial pathfinding and the vehicle system provides physical movement — HTN decides “what to do” while navigation and vehicles handle “how to move”.