64 lines
2.0 KiB
GDScript
64 lines
2.0 KiB
GDScript
class_name ActionTargetResolver
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extends RefCounted
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func resolve(
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npc: SimNPC, origin: Vector3, simulation_manager: Node, active_world_adapter: Node
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) -> Dictionary:
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var definition := SimulationDefinitions.get_action(npc.current_task)
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if definition == null:
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return {}
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match definition.target_type:
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SimulationIds.TARGET_RESOURCE:
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return _resolve_resource(
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npc, origin, definition, simulation_manager, active_world_adapter
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)
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SimulationIds.TARGET_ACTIVITY:
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return active_world_adapter.get_activity_target(npc.current_task, origin)
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SimulationIds.TARGET_FREE:
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return {
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"target_id": "", "position": origin + simulation_manager.get_wander_offset(npc.id)
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}
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return {}
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func _resolve_resource(
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npc: SimNPC,
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origin: Vector3,
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definition: ActionDefinition,
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simulation_manager: Node,
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active_world_adapter: Node
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) -> Dictionary:
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var best: Dictionary = {}
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var best_score := INF
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for candidate in active_world_adapter.get_resource_candidates(definition.resource_action_id):
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var node_id := StringName(candidate["target_id"])
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var state: ResourceStateRecord = simulation_manager.get_resource_state(node_id)
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if state == null or not state.can_npc_use() or not state.is_available_for(npc.id):
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continue
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var position: Vector3 = candidate["position"]
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var score := score_resource_candidate(npc, origin, position, state)
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if score < best_score:
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best_score = score
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best = candidate
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if best.is_empty():
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return {}
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var target_id := StringName(best["target_id"])
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if not simulation_manager.reserve_resource(target_id, npc.id):
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return {}
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return best
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func score_resource_candidate(
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npc: SimNPC, origin: Vector3, position: Vector3, state: ResourceStateRecord
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) -> float:
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var distance := origin.distance_to(position)
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var comfort_overage := maxf(distance - state.get_comfort_distance(), 0.0)
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var risk_weight := 20.0 + maxf(100.0 - npc.energy, 0.0) * 0.2
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return (
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distance
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+ comfort_overage * 2.5
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+ state.get_safety_risk() * risk_weight
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- state.get_discovery_priority()
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)
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