"""Create reasoning_engine
Generated by Phi-Octopus Eden
2025-11-05 20:24:46.412460
"""

```python
from typing import List, Dict, Any


class ReasoningEngine:
    """
    A simple reasoning engine that can solve limited reasoning problems.
    
    This class is designed to handle basic problem-solving tasks by maintaining a set of rules and facts,
    and using these to infer new information or solutions.

    Args:
        initial_knowledge: A dictionary containing the initial knowledge base with key-value pairs
                           representing fact attributes and their corresponding values.
        inference_rules: A list of dictionaries, where each dictionary contains keys for antecedents (conditions)
                         that must be true in order for the rule's consequent (solution) to apply.

    Methods:
        __add_knowledge(fact): Adds a new fact to the knowledge base.
        __infer(new_facts): Attempts to infer new facts based on the current knowledge and inference rules.
        solve(problems: List[str]) -> Dict[str, Any]: Solves multiple reasoning problems by returning
                                                        a dictionary of inferred solutions for each problem.
    """
    
    def __init__(self, initial_knowledge: Dict[str, Any], inference_rules: List[Dict[str, List[Any]]]):
        self.knowledge_base = initial_knowledge
        self.inference_rules = inference_rules

    def __add_knowledge(self, fact: Dict[str, Any]) -> None:
        """Add a new fact to the knowledge base."""
        self.knowledge_base.update(fact)

    def __infer(self, new_facts: List[Dict[str, Any]]) -> Dict[str, Any]:
        """
        Infer new facts based on existing knowledge and inference rules.
        
        Args:
            new_facts: A list of new facts that might be relevant to the inference process.

        Returns:
            A dictionary containing inferred solutions.
        """
        inferred_solutions = {}
        for rule in self.inference_rules:
            if all(fact.get(key) == value for key, value in rule['antecedents'].items()):
                solution = rule['consequent']
                # Update the knowledge base with new facts and inferred results
                for k, v in solution.items():
                    inferred_solutions[k] = self.__add_knowledge({k: v})
        return inferred_solutions

    def solve(self, problems: List[str]) -> Dict[str, Any]:
        """
        Solve multiple reasoning problems.

        Args:
            problems: A list of strings representing the reasoning problems to be solved.
        
        Returns:
            A dictionary where each key is a problem identifier and the value is the inferred solution for that problem.
        """
        solutions = {}
        for idx, problem in enumerate(problems):
            # Simulate solving by adding a relevant fact
            self.__add_knowledge({'problem': f'Problem {idx + 1}'})
            new_facts = [{'relevant_fact': 'A relevant fact'}]
            inferred_solutions = self.__infer(new_facts)
            solutions[f'Problem {idx + 1}'] = inferred_solutions
        return solutions


# Example usage:
if __name__ == '__main__':
    initial_knowledge = {
        'temperature': '32 degrees',
        'pressure': '500 hPa'
    }
    
    inference_rules = [
        {
            'antecedents': {'temperature': '32 degrees'},
            'consequent': {'humidity': '80%'}
        },
        {
            'antecedents': {'pressure': '500 hPa'},
            'consequent': {'altitude': '4,000 meters'}
        }
    ]
    
    reasoning_engine = ReasoningEngine(initial_knowledge=initial_knowledge, inference_rules=inference_rules)
    
    problems_to_solve = ['Problem 1', 'Problem 2']
    solutions = reasoning_engine.solve(problems_to_solve)
    print(solutions)

```