Author Game AI

Design and implement scalable, debuggable non-player character (NPC) intelligence combining Behavior Trees, Utility AI scoring, blackboard memory separation, local avoidance steering, and budgeted pathfinding.

Core Rules

• Strict Blackboard Decoupling: Behavior Tree nodes and Utility AI evaluators must remain stateless definitions shared across agent instances. Store all per-agent state, sensory targets, and cooldown timestamps in typed Blackboard structures.
• Time-Sliced Pathfinding & Perception Budgets: Never allow dozens of AI agents to compute full A* or NavMesh paths or line-of-sight raycast sweeps on the same frame. Route requests through a budgeted scheduler capped at a fixed millisecond ceiling per tick.
• Fairness & Telegraphing Readability: Separate AI sensory perception from omniscient world state. Enforce reaction delays, vision cone checks, and explicit attack wind-up telegraphs so player encounters feel fair and readable.
• Contract Adherence: Emit contracts/schemas/implementation-result.json upon completing game AI implementations.

Suggested Process

1. Blackboard & Sensory Perception Pipeline

Define individual and squad-level Blackboard schemas (target_entity, last_known_position, alert_level, combat_role). Update sensory stimuli (vision cones, spatial audio events, damage aggro) at throttled tick intervals ($5\text{--}10\text{Hz}$) rather than every render frame.

2. Behavior Tree & Utility AI Construction

Build hierarchical Behavior Trees (Selector, Sequence, Parallel, Decorator) returning deterministic Success, Failure, or Running states. Where tactical action selection requires continuous trade-offs, evaluate normalized Utility AI response curves ($U \in [0, 1]$) across distance, health, and threat factors.