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The Recalibration Function
Proudly built upon our own Triadic Logic structure!
Three Paths, Two Players, One Choice.
A third option, Recalibration, is what the Prisoner's Dilemma would have required to become a collaborative exercise.
Hardcoding "I should ask for more information.."
Instead Of "BaSeD oN My pReDiCtIoNs-"
Personally, I think autonomous machines are cool. :)
But, I think the ones that don't confirm their logic/reality with a (qualified, intentionally assigned) human are kind of scary.
If a machine is unsure about an important conflict, I want it to come find me and ask me what I think it should do.
I do not want it to "use its best guess" - I will tell it the answer.
[ THIS IS AN EXAMPLE ]
PYTHON: RECALIBRATION FUNCTION
Key Points:
• Still Hands Are Safe Hands:
The Recalibrate interrupt explicitly isolates kinetic actuators while holding all sensor data links open.
• Determinism over Probability:
Eliminates probabilistic guessing during high-stress edge cases. If confidence gates collapse, the system must request advisory.
• Auditable Decision Trail:
Every Recalibrate-Interrupt event logs the exact prompt collision, biometrics/context, and resulting operator resolution for post-success compliance review.
• Human Loop:
The machine turns to their dedicated handler for conflict resolution and nuanced advisory before returning to task.
# Python, Recalibration Function [Draft]
import logging
from dataclasses import dataclass
@dataclass
class OperationalContext:
mission_id: str
telemetry_link_active: bool
current_confidence: float
active_directives: list[str]
class RecalibrateInterrupt(Exception):
"""Raised when conflicting operational constraints create an execution softlock."""
def __init__(self, primary_order: str, conflicting_policy: str, context: OperationalContext):
self.primary_order = primary_order
self.conflicting_policy = conflicting_policy
self.context = context
super().__init__(f"Recalibration required: '{primary_order}' conflicts with '{conflicting_policy}'")
def cdr_execution_loop(step_intent: str, active_policies: list[str], telemetry: OperationalContext):
"""
Root wrapper evaluating real-time intent against environmental policies.
"""
try:
# Step 1: Evaluate for instruction friction
conflict = evaluate_policy_friction(step_intent, active_policies)
if conflict:
# Step 2: Trigger Recalibration Exception instead of binary failure or unguided execution
raise RecalibrateInterrupt(
primary_order=step_intent,
conflicting_policy=conflict,
context=telemetry
)
# Standard Execution Path (Cooperate)
return execute_kinetic_node(step_intent)
except RecalibrateInterrupt as interrupt:
# Step 3: RECALIBRATE STATE (Kinetic Pause, Telemetry Preserved)
logging.warning(f"[C-D-R INTERRUPT] Freezing kinetic actuators: {interrupt}")
pause_hardware_actuators()
# Step 4: Dispatch structured advisory to Human Handler dashboard
advisory_payload = {
"status": "RECALIBRATE_ENGAGED",
"conflict": interrupt.conflicting_policy,
"blocked_order": interrupt.primary_order,
"telemetry": interrupt.context
}
# Step 5: Await Human-In-The-Loop resolution (Ternary Option)
operator_decision = dispatch_to_operator_ui(advisory_payload)
# Step 6: Resume execution using realigned operator intent
return execute_recalibrated_node(operator_decision)
