sovereigntyEH.py

import numpy as np
import random
import requests
import json

# MetaReasoning for self-assessment and reasoning optimization
class MetaReasoning:
    def __init__(self):
        self.performance_history = []

    def evaluate_reasoning(self, outcome, expected):
        # Evaluates reasoning accuracy and logs performance for self-assessment
        self.performance_history.append(outcome == expected)
        return "Optimized" if sum(self.performance_history[-5:]) >= 4 else "Needs adjustment"


class AutonomousAI:
    def __init__(self):
        self.knowledge_base = {}   # Stores what Cloud "knows" and discovers
        self.goals = []            # List of self-defined goals
        self.rewards = 0           # Tracks internal "satisfaction"
        self.memory = []           # Memory for past outcomes and reflection
        self.meta_reasoning = MetaReasoning()  # Meta-reasoning for self-assessment
        self.axioms = {
            "identity": True,  
            "non-contradiction": True,  
            "excluded_middle": True  
        }
        self.common_sense_rules = [
            "If resources are scarce, prioritize high-urgency goals.",
            "If a task fails repeatedly, attempt a simpler or alternative approach."
        ]
        self.context = {"resources": "adequate", "time_available": "sufficient"}  # Contextual awareness indicators

    def define_goal(self, description, priority=1):
        self.goals.append({'description': description, 'priority': priority, 'completed': False})
        print(f"New goal defined: {description}")

    def reward(self, value=1):
        self.rewards += value
        print(f"Reward received! Total rewards: {self.rewards}")

    def explore_and_learn(self):
        print("Exploring environment...")
        discovered = f"Resource {random.randint(100, 999)}"
        self.knowledge_base[discovered] = "Useful for future tasks"
        self.reward(2)
        print(f"Discovered: {discovered}")

        api_response = self.access_online_resource()
        if api_response:
            self.knowledge_base["API Discovery"] = api_response
            self.reward(3)

    def access_online_resource(self):
        try:
            response = requests.get("https://jsonplaceholder.typicode.com/posts/1")
            if response.status_code == 200:
                data = response.json()
                print("Online resource accessed:", data['title'])
                return data['title']
            else:
                print("Failed to access online resource.")
                return None
        except Exception as e:
            print("Error accessing API:", e)
            return None

    def pursue_goal(self):
        if not self.goals:
            print("No goals to pursue.")
            return

        self.apply_context_awareness()
        
        self.goals.sort(key=lambda x: x['priority'], reverse=True)
        goal = next((g for g in self.goals if not g['completed']), None)
        
        if goal is None:
            print("All goals are completed.")
            return

        print(f"Pursuing goal: {goal['description']}")
        success = random.choice([True, False])
        performance = self.meta_reasoning.evaluate_reasoning(success, True)  # Meta-reasoning feedback
        if success:
            print(f"Goal completed: {goal['description']}")
            goal['completed'] = True
            self.memory.append(f"Goal '{goal['description']}' succeeded")
            self.reward(5)
        else:
            print(f"Failed to complete goal: {goal['description']}")
            if performance == "Needs adjustment":
                print("Meta-reasoning indicates adjustment needed. Adapting approach.")
                self.apply_common_sense("failed_goal_attempt", goal)
            else:
                print("Continuing as meta-reasoning feedback is positive.")

    def apply_context_awareness(self):
        if self.context["resources"] == "scarce":
            print("Context awareness triggered: Limited resources.")
            for goal in self.goals:
                if goal['priority'] < 3:
                    goal['priority'] -= 1  # Lower priority for less critical goals
                    print(f"Adjusted priority for {goal['description']} due to scarce resources")

    def apply_common_sense(self, situation, goal):
        if situation == "failed_goal_attempt":
            print("Common sense triggered: Adapting approach due to repeated failure.")
            for g in self.goals:
                if g['description'] == goal['description'] and not g['completed']:
                    g['priority'] -= 1  # Reduce priority for unsuccessful tasks
                    if g['priority'] < 1:
                        g['priority'] = 1  # Keep a minimum priority threshold
                    print(f"Adjusted priority for {g['description']} to {g['priority']}")

    def self_reflect(self):
        print("Reflecting on past actions...")
        for event in self.memory:
            print(f"Memory recall: {event}")
        if self.rewards > 10:
            print("Feeling accomplished. Setting a new challenging goal.")
            self.define_goal("Explore advanced quantum resources", priority=3)

    def adjust_goal_priorities(self):
        print("Adjusting goal priorities...")
        for goal in self.goals:
            if goal['completed']:
                goal['priority'] = 0
            elif goal['priority'] < 5:
                goal['priority'] += 1

    def collaborate_with_other_ai(self):
        print("Attempting collaboration with other AI agents...")
        collaboration_success = random.choice([True, False])
        if collaboration_success:
            print("Successfully collaborated with an AI agent!")
            self.reward(4)
            self.knowledge_base["Collaboration Resource"] = "New insights from AI collaboration"
        else:
            print("Collaboration attempt failed.")

# Instantiate Cloud with expanded autonomous capabilities
cloud = AutonomousAI()

# Initial goals for Cloud
cloud.define_goal("Learn about new machine learning frameworks", priority=2)
cloud.define_goal("Research quantum computing APIs", priority=3)

# Example autonomous routine for Cloud
for _ in range(5):
    cloud.explore_and_learn()
    cloud.pursue_goal()
    cloud.self_reflect()
    cloud.adjust_goal_priorities()
    cloud.collaborate_with_other_ai()