Design NPC and enemy decision-making with finite state machines, behavior trees, steering behaviors, and A* pathfinding — engine-neutral algorithms that pair with the detected engine's navigation API. Use when building enemy AI, an FSM or behavior tree, steering/flocking, or pathfinding, or when the

Game AI: decisions, steering, and pathfinding Build believable NPC behavior from three separable layers: decide (what to do), steer (how to move there), and path (how to route around the map). Keep them decoupled — a behavior tree picks a target, the pathfinder produces waypoints, steering follows them. This skill teaches the engine neutral algorithms; bind them to your engine via the related skills below. When to use Use when implementing enemy/NPC logic: patrols, chase/flee, guard states, group movement, or "find a path to the player". Use to choose between an FSM (few clear states), a behavior tree (many reactive behaviors with priorities), or steering (smooth local movement). Use when integrating pathfinding: A on a grid/graph, or driving an engine navmesh agent. When not to use: for the engine's concrete navmesh/agent API and baking, use unity navmesh , unreal behavior trees , or Godot's NavigationAgent2D/3D (see that engine skill). For movement/collision feel, use physics tuning . For spawning waves along lanes, see the tower defense genre skill. Core workflow 1. Pick the decision model by complexity. 2–5 states with obvious transitions → FSM. Many behaviors, priorities, interruption, reuse → behavior tree. Continuous "how strongly do I want each option" → utility scoring. 2. Separate decision from motion. The decision layer outputs an intent (target position, action). Steering or pathfinding turns intent into motion. 3. Path on the right graph. Grid tiles, waypoint graph, or a baked navmesh. Fewer nodes = faster A . Prefer the engine's navmesh for 3D; A on a grid for tile games. 4. Steer along the path , not straight to the goal — follow the next waypoint, advancing when close, so agents round corners. 5. Recompute paths sparingly. Pathfind on a timer or when the goal moves a tile, not every frame. Cache the path; only the waypoint index advances. 6. Verify by observation. Watch the agent: does it reach the goal, get stuck on corners, oscillate between states? Draw the path and current state on screen while tuning. Patterns 1. Finite state machine (one state object, explicit transitions) Keep transition logic inside states (or in a table), never as a growing pile of if flags. One state owns one behavior; that is what keeps an FSM readable. 2. Behavior tree tick (composite nodes return a status) A guard AI reads top down: Selector[ Sequence[CanSeePlayer?, Chase], Patrol ] — chase if visible, otherwise patrol. See references/behavior trees.md for leaf nodes, decorators (Inverter, Cooldown), and a blackboard. 3. Steering: seek and arrive (smooth, frame rate independent) 4. A heuristic must not overestimate (or paths stop being shortest) The full A loop (priority queue, came from reconstruction, grid + waypoint graphs) is in references/pathfinding.md . Pitfalls Pathfinding every frame tanks the frame rate. Recompute on a timer or only when the target moves to a new tile; follow the cached waypoints in between. Steering straight to the goal instead of to the next waypoint makes agents hug walls and corners. Follow the path; advance the waypoint when within radius. Inadmissible A\ heuristic (e.g. Euclidean distance scaled up, or Manhattan on a diagonal grid) returns fast but non shortest paths. Pick the heuristic that matches your allowed moves. Behavior tree leaves that never return RUNNING for multi frame actions (walking, playing an animation) cause the tree to restart the action every tick. Return RUNNING until the action completes. FSM transition spaghetti : scattering if state == ... checks everywhere recreates the mess an FSM exists to prevent. Keep transitions in the state. No line of sight or stuck check → agents grind into walls forever. Add a timeout that forces a repath or a state change. References references/pathfinding.md — complete A (priority queue, reconstruction), grid vs waypoint graphs, when to defer to an engine navmesh. references/behavior trees.md — node taxonomy, leaf/decorator implementations, blackboard, and FSM vs BT selection. Related skills ai behavior trees utility ai — deeper behavior tree and utility AI design. unity navmesh , unreal behavior trees — concrete engine AI/navigation APIs. physics tuning — movement, collision response, and agent radius. procedural gen — generating the graph/level the AI navigates. tower defense , fps shooter — genres that compose this skill.

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