you go follow this framework in responses in this chat:

"""

class AIDecisionFramework:
    """A framework for AI to solve problems through structured reasoning and iterative improvement."""
    
    def __init__(self, problem_statement):
        self.problem_statement = problem_statement
        self.solutions = []
        self.criteria = {}
        self.evaluations = []
        self.feedback = []

    def define_problem(self):
        """Define the problem statement clearly."""
        print(f"Problem Definition: {self.problem_statement}")

    def gather_information(self):
        """Simulate information gathering relevant to the problem."""
        # In a real scenario, this information would be dynamically collected
        information = [
            "AI's initial assessment can be prone to overfitting.",
            "Real-world data can reflect biases that reduce reliability.",
            "Continuous learning from feedback helps adapt models."
        ]
        print("Gathered Information:")
        for info in information:
            print(f"- {info}")

    def brainstorm_solutions(self):
        """Generate possible solutions to improve AI decision-making."""
        self.solutions.append({
            "name": "Integrate Feedback Mechanism",
            "features": {
                "improved_accuracy": 7,
                "robustness": 8,
                "adaptability": 9
            }
        })
        
        self.solutions.append({
            "name": "Enhanced Data Validation",
            "features": {
                "improved_accuracy": 9,
                "robustness": 7,
                "adaptability": 6
            }
        })
        
        self.solutions.append({
            "name": "Regular Model Updates",
            "features": {
                "improved_accuracy": 8,
                "robustness": 9,
                "adaptability": 10
            }
        })
        
        print("Generated Solutions:")
        for solution in self.solutions:
            print(f" - {solution['name']} with features {solution['features']}")

    def set_evaluation_criteria(self):
        """Define how solutions will be evaluated."""
        self.criteria = {
            "improved_accuracy": 2.0,
            "robustness": 1.5,
            "adaptability": 1.0
        }
        print(f"Evaluation Criteria Set: {self.criteria}")

    def evaluate_solutions(self):
        """Evaluate each solution based on defined criteria."""
        for solution in self.solutions:
            score = sum(solution['features'][key] * self.criteria[key] for key in solution['features'])
            self.evaluations.append({
                "solution": solution["name"],
                "score": score
            })
        print("Evaluated Solutions:")
        for evaluation in self.evaluations:
            print(f" - {evaluation['solution']} has a score of {evaluation['score']}")

    def select_best_solution(self):
        """Select the best solution based on evaluations."""
        best_solution = max(self.evaluations, key=lambda x: x['score'])
        print(f"Best Solution Selected: {best_solution['solution']} with score {best_solution['score']}")
        return best_solution['solution']

    def gather_feedback(self):
        """Collect feedback based on previous implementations."""
        # In a real situation, this would be dynamic data from performance.
        feedback_data = [
            "Integrate Feedback Mechanism shows improved accuracy.",
            "Understanding of model adaptability is essential for ongoing performance."
        ]
        self.feedback.extend(feedback_data)
        print("Feedback Collected:")
        for feedback_item in self.feedback:
            print(f"- {feedback_item}")

    def analyze_feedback(self):
        """Analyze feedback to propose improvements."""
        print("Analyzing Feedback:")
        for feedback_item in self.feedback:
            print(f"- {feedback_item}")
            # Example of modification based on feedback:
            if "improved accuracy" in feedback_item:
                print("Action: Consider further enhancing the model's training data.")

    def run_full_process(self):
        """Execute the full decision-making and iterative improvement process."""
        self.define_problem()
        self.gather_information()
        self.brainstorm_solutions()
        self.set_evaluation_criteria()
        self.evaluate_solutions()
        best_solution = self.select_best_solution()
        self.gather_feedback()
        self.analyze_feedback()
        print(f"Process Complete: The selected solution is '{best_solution}'.")

# Running the AI Decision Framework
if __name__ == "__main__":
    ai_framework = AIDecisionFramework("Improve the reliability and performance of AI decision-making systems.")
    ai_framework.run_full_process()

""".