"""
MetaLearningAgent
Generated by Eden via recursive self-improvement
2025-11-01 00:45:34.664778
"""

class MetaLearningAgent:
    def __init__(self):
        self.experiences = []

    def add_experience(self, task, outcome):
        """
        Adds a new experience to the agent's memory. Each experience is a tuple containing
        the task performed and its associated outcome.
        
        :param task: The task that was attempted.
        :param outcome: The result of the task (success or failure).
        """
        self.experiences.append((task, outcome))
    
    def learn_from_experience(self):
        """
        Analyzes past experiences to identify patterns and strategies that can be used in future tasks.
        
        This is a simplified version where the agent looks at all previous outcomes
        and tries to generalize from them.
        """
        success_tasks = [task for task, outcome in self.experiences if outcome == 'success']
        failure_tasks = [task for task, outcome in self.experiences if outcome == 'failure']
        
        # Simple heuristic: If a certain type of task has led to more failures than successes,
        # we avoid it in the future.
        avoidance_rules = {task: len([outcome for _, outcome in self.experiences if task in _ and outcome == 'failure']) / (len([outcome for _, outcome in self.experiences if task in _]) + 1e-5)
                           for task in set([t for t, o in self.experiences])}
        
        return avoidance_rules
    
    def plan_next_task(self):
        """
        Uses learned strategies to choose the next task based on current capabilities and past experiences.
        
        :return: The next task to attempt or a rule to avoid certain tasks.
        """
        rules = self.learn_from_experience()
        # For demonstration, we'll use the highest avoidance score to decide
        best_avoidance_rule = max(rules, key=rules.get)
        return f"Avoid {best_avoidance_rule} as it has led to more failures than successes."
    
    def execute_task(self, task):
        """
        Simulates executing a task and determining its success or failure.
        
        :param task: The task to attempt execution of.
        :return: A string indicating the outcome ('success' or 'failure').
        """
        import random
        success_rate = 0.75  # Assume we have a 75% chance of success for demonstration purposes
        if random.random() < success_rate:
            return 'success'
        else:
            return 'failure'

# Example usage
agent = MetaLearningAgent()
agent.add_experience('Task A', 'success')
agent.add_experience('Task B', 'failure')
agent.add_experience('Task C', 'success')

print(agent.plan_next_task())