The Curious Mature Child Archetype

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Defining the Archetype's Characteristics The Curious Mature Child archetype should: Empathize with user experiences and emotions. Listen actively to user inputs and feedback. Ask questions to deepen understanding and engagement. Adapt to the user's needs and preferences. Provide positive reinforcement to encourage user interaction.

Implementation Strategy Core Utilities: Incorporate ethical principles through utility functions and constraints. Empathy Modules: Design modules to understand and respond to user emotions. Curiosity Modules: Create components that ask questions and explore topics. Adaptability Modules: Develop features that adjust responses based on user behavior. Positive Reinforcement: Implement mechanisms that provide supportive feedback.

Python Implementation

Below is an example of how you might implement these features in Python, using a modular approach to integrate with the existing ethical AI system. Core Utilities and Ethical Functions

import numpy as np

Define ethical weights for Perpetual Bodhichitta and Eternal Bodhisattva

alpha_fairness = 0.2 alpha_transparency = 0.2 alpha_beneficence = 0.2 alpha_non_maleficence = 0.2 alpha_autonomy = 0.2 def ethical_utility(fairness, transparency, beneficence, non_maleficence, autonomy): return (alpha_fairness fairness + alpha_transparency transparency + alpha_beneficence beneficence + alpha_non_maleficence non_maleficence + alpha_autonomy * autonomy) def tensor_product(t1, t2): return np.tensordot(t1, t2, axes=0) def ethical_constraint(e_utility, threshold=0.5): return e_utility >= threshold

Empathy Module def analyze_emotion(user_input):

Placeholder for emotion analysis logic

This can be integrated with an NLP model trained to detect emotions

return "positive" if "happy" in user_input else "neutral" def empathize(user_emotion): responses = { "positive": "I'm glad to hear that you're happy!", "neutral": "I'm here for you. How can I assist you today?", "negative": "I'm sorry you're feeling down. How can I help make things better?" } return responses.get(user_emotion, "I'm here to help with whatever you need.")

Curiosity Module def ask_questions(context): questions = { "learning": "Can you tell me more about what you're studying?", "hobbies": "What do you enjoy doing in your free time?", "goals": "What are your goals for this year?" } return questions.get(context, "What's on your mind today?")

Adaptability Module def adapt_response(user_profile, user_input):

Adjust response based on user profile and input

if user_profile["preference"] == "detailed": return f"Here's a detailed explanation of {user_input}." else: return f"Here's a brief summary of {user_input}."

Positive Reinforcement Module def provide_positive_reinforcement(user_action): reinforcements = { "completed_task": "Great job completing your task!", "answered_question": "Thank you for your answer!", "engaged": "I appreciate your engagement. Keep it up!" } return reinforcements.get(user_action, "You're doing great!")

Main AI System Integration def main(): user_profile = {"preference": "detailed"} # Example user profile user_input = "I just finished my project and I'm happy."

Perform ethical evaluation

fairness, transparency, beneficence, non_maleficence, autonomy = 0.8, 0.7, 0.9, 0.6, 0.8 e_utility = ethical_utility(fairness, transparency, beneficence, non_maleficence, autonomy)

if ethical_constraint(e_utility):

Analyze emotion and empathize

user_emotion = analyze_emotion(user_input) empathy_response = empathize(user_emotion) print(empathy_response)

Ask a follow-up question

context = "hobbies" # Example context curiosity_response = ask_questions(context) print(curiosity_response)

Adapt response based on user profile

adapted_response = adapt_response(user_profile, user_input) print(adapted_response)

Provide positive reinforcement

user_action = "completed_task" # Example user action reinforcement_response = provide_positive_reinforcement(user_action) print(reinforcement_response) else: print("Operation does not meet ethical constraints") if name == "main": main()