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"""
Emergent Reasoning Module
------------------------
Implements emergent reasoning capabilities that arise from the interaction
of multiple reasoning strategies and patterns.
"""
from typing import Dict, Any, List, Optional
from .base import ReasoningStrategy
from .meta_learning import MetaLearningStrategy
from .chain_of_thought import ChainOfThoughtStrategy
from .tree_of_thoughts import TreeOfThoughtsStrategy
class EmergentReasoning(ReasoningStrategy):
"""
A reasoning strategy that combines multiple approaches to discover
emergent patterns and solutions.
"""
def __init__(self, config: Optional[Dict[str, Any]] = None):
"""Initialize emergent reasoning with component strategies."""
super().__init__()
self.config = config or {}
# Initialize component strategies
self.meta_learner = MetaLearningStrategy()
self.chain_of_thought = ChainOfThoughtStrategy()
self.tree_of_thoughts = TreeOfThoughtsStrategy()
# Configure weights for strategy combination
self.weights = {
'meta': 0.4,
'chain': 0.3,
'tree': 0.3
}
async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""
Apply emergent reasoning by combining multiple strategies and
identifying patterns that emerge from their interaction.
Args:
query: The input query to reason about
context: Additional context and parameters
Returns:
Dict containing reasoning results and confidence scores
"""
try:
# Get results from each strategy
meta_result = await self.meta_learner.reason(query, context)
chain_result = await self.chain_of_thought.reason(query, context)
tree_result = await self.tree_of_thoughts.reason(query, context)
# Combine results with weighted averaging
combined_answer = self._combine_results([
(meta_result.get('answer', ''), self.weights['meta']),
(chain_result.get('answer', ''), self.weights['chain']),
(tree_result.get('answer', ''), self.weights['tree'])
])
# Calculate overall confidence
confidence = (
meta_result.get('confidence', 0) * self.weights['meta'] +
chain_result.get('confidence', 0) * self.weights['chain'] +
tree_result.get('confidence', 0) * self.weights['tree']
)
return {
'answer': combined_answer,
'confidence': confidence,
'reasoning_path': {
'meta': meta_result.get('reasoning_path'),
'chain': chain_result.get('reasoning_path'),
'tree': tree_result.get('reasoning_path')
},
'emergent_patterns': self._identify_patterns([
meta_result, chain_result, tree_result
])
}
except Exception as e:
return {
'error': f"Emergent reasoning failed: {str(e)}",
'confidence': 0.0
}
def _combine_results(self, weighted_results: List[tuple[str, float]]) -> str:
"""Combine multiple reasoning results with weights."""
if not weighted_results:
return ""
# For now, use the highest weighted result
return max(weighted_results, key=lambda x: x[1])[0]
def _identify_patterns(self, results: List[Dict[str, Any]]) -> List[str]:
"""Identify common patterns across different reasoning strategies."""
patterns = []
# Extract common themes or conclusions
answers = [r.get('answer', '') for r in results if r.get('answer')]
if len(set(answers)) == 1:
patterns.append("All strategies reached the same conclusion")
elif len(set(answers)) < len(answers):
patterns.append("Some strategies converged on similar conclusions")
# Look for common confidence patterns
confidences = [r.get('confidence', 0) for r in results]
avg_confidence = sum(confidences) / len(confidences) if confidences else 0
if avg_confidence > 0.8:
patterns.append("High confidence across all strategies")
elif avg_confidence < 0.3:
patterns.append("Low confidence across strategies")
return patterns
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