Files

94 lines
3.0 KiB
GDScript

class_name ActionTargetResolver
extends RefCounted
func resolve(
npc: SimNPC, origin: Vector3, simulation_manager: Node, active_world_adapter: Node
) -> Dictionary:
var definition := SimulationDefinitions.get_action(npc.current_task)
if definition == null:
return {}
match definition.target_type:
SimulationIds.TARGET_RESOURCE:
return _resolve_resource(
npc, origin, definition, simulation_manager, active_world_adapter
)
SimulationIds.TARGET_ACTIVITY:
return _resolve_activity(npc, origin, simulation_manager, active_world_adapter)
SimulationIds.TARGET_FREE:
return {
"target_id": "", "position": origin + simulation_manager.get_wander_offset(npc.id)
}
return {}
func _resolve_resource(
npc: SimNPC,
origin: Vector3,
definition: ActionDefinition,
simulation_manager: Node,
active_world_adapter: Node
) -> Dictionary:
var best: Dictionary = {}
var best_score := INF
for candidate in active_world_adapter.get_resource_candidates(definition.resource_action_id):
var node_id := StringName(candidate["target_id"])
var state: ResourceStateRecord = simulation_manager.get_resource_state(node_id)
if state == null or not state.can_npc_use() or not state.is_available_for(npc.id):
continue
var position: Vector3 = candidate["position"]
var score := score_resource_candidate(npc, origin, position, state)
if score < best_score:
best_score = score
best = candidate
if best.is_empty():
return {}
var target_id := StringName(best["target_id"])
if not simulation_manager.reserve_resource(target_id, npc.id):
return {}
return best
func _resolve_activity(
npc: SimNPC, origin: Vector3, simulation_manager: Node, active_world_adapter: Node
) -> Dictionary:
if not active_world_adapter.has_method("get_activity_candidates"):
return active_world_adapter.get_activity_target(npc.current_task, origin)
var best: Dictionary = {}
var best_distance := INF
var candidates: Array[Dictionary] = active_world_adapter.get_activity_candidates(npc.current_task)
for candidate in candidates:
var target_id := StringName(candidate["target_id"])
var capacity := maxi(int(candidate.get("capacity", 1)), 1)
if (
simulation_manager.has_method("get_activity_target_claim_count")
and simulation_manager.get_activity_target_claim_count(target_id, npc.id) >= capacity
):
continue
var position: Vector3 = candidate["position"]
var distance := origin.distance_squared_to(position)
if distance < best_distance:
best_distance = distance
best = candidate
if not best.is_empty():
return best
if not candidates.is_empty():
return {}
return active_world_adapter.get_activity_target(npc.current_task, origin)
func score_resource_candidate(
npc: SimNPC, origin: Vector3, position: Vector3, state: ResourceStateRecord
) -> float:
var distance := origin.distance_to(position)
var comfort_overage := maxf(distance - state.get_comfort_distance(), 0.0)
var risk_weight := 20.0 + maxf(100.0 - npc.energy, 0.0) * 0.2
return (
distance
+ comfort_overage * 2.5
+ state.get_safety_risk() * risk_weight
- state.get_discovery_priority()
)