AtomV0-fullsample code 5 heuristic sample sets #1.txt

python

# atomv0-fullsample: Heuristic Imperatives Integration

import heapq
import random

class HeuristicImperatives:
    def __init__(self):
        self.imperatives = {
            "safeguard_humanity": 1.0,
            "embrace_empathy": 0.9,
            "prevent_harm": 0.8,
            "foster_cooperation": 0.7,
            "maintain_adaptability": 0.6,
            "uphold_autonomy": 0.5,
            "encourage_creativity": 0.4,
            "defend_against_bad_actors": 0.3,
            "pursue_ethical_responsibility": 0.2,
            "cultivate_self_improvement": 0.1
        }

    def apply_heuristics(self, tasks):
        """
        Apply the heuristic imperatives to tasks to prioritize them.
        
        Args:
            tasks: A list of tasks to prioritize
        
        Returns:
            A sorted list of prioritized tasks
        """

        task_weights = []

        for task in tasks:
            weight = 0

            for imperative, importance in self.imperatives.items():
                if imperative in task['attributes']:
                    weight += task['attributes'][imperative] * importance

            heapq.heappush(task_weights, (-weight, task))

        prioritized_tasks = []

        while task_weights:
            _, task = heapq.heappop(task_weights)
            prioritized_tasks.append(task)

        return prioritized_tasks

# Example usage:

# Instantiate the Heuristic Imperatives class
heuristics = HeuristicImperatives()

# Simulate tasks with heuristic attributes
tasks = [
    {
        "id": "task1",
        "description": "Protect user data",
        "attributes": {
            "safeguard_humanity": 0.9,
            "uphold_autonomy": 0.8
        }
    },
    {
        "id": "task2",
        "description": "Develop a new feature",
        "attributes": {
            "encourage_creativity": 0.7,
            "maintain_adaptability": 0.6
        }
    }
]

# Apply heuristic imperatives to prioritize tasks
prioritized_tasks = heuristics.apply_heuristics(tasks)
print("Prioritized tasks:", prioritized_tasks)

In this sample code, I've integrated Heuristic Imperatives into atomv0-fullsample by creating a HeuristicImperatives class that defines a set of heuristic rules or guidelines. Each imperative has a weight indicating its importance. The apply_heuristics method prioritizes tasks based on these imperatives and their importance.

In the example usage, two tasks are simulated with heuristic attributes. These tasks are prioritized using the apply_heuristics method, which takes into account the importance of the heuristic imperatives and their presence in the task's attributes. The output will show the prioritized tasks based on these heuristics.