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reasoning/__init__.py

@@ -1,60 +1,55 @@
 """
 Advanced Reasoning Engine for Multi-Model System
 ---------------------------------------------
-A highly sophisticated reasoning system combining:
+A highly sophisticated reasoning system combining multiple reasoning strategies.
 
 Core Reasoning:
 1. Chain of Thought (CoT)
 2. Tree of Thoughts (ToT)
-3. Graph of Thoughts (GoT)
-4. Recursive Reasoning
-5. Analogical Reasoning
-6. Meta-Learning
+3. Recursive Reasoning
+4. Analogical Reasoning
+5. Meta-Learning
+6. Local LLM
 
 Advanced Reasoning:
 7. Neurosymbolic Reasoning
-8. Counterfactual Reasoning
-9. State Space Search
-10. Probabilistic Reasoning
-11. Causal Inference
-12. Temporal Reasoning
+8. Bayesian Reasoning
+9. Quantum Reasoning
+10. Emergent Reasoning
+11. Multimodal Reasoning
+12. Specialized Reasoning
 
 Learning & Adaptation:
-13. Online Learning
-14. Transfer Learning
-15. Meta-Learning
-16. Active Learning
-
-Robustness Features:
-17. Uncertainty Quantification
-18. Error Recovery
-19. Consistency Checking
-20. Bias Detection
+13. Market Analysis
+14. Portfolio Optimization
+15. Venture Strategies
+16. Monetization Strategies
 """
 
 from .base import ReasoningStrategy
 from .multimodal import MultiModalReasoning
 from .bayesian import BayesianReasoning
-from .quantum import QuantumReasoning, QuantumInspiredStrategy
+from .quantum import QuantumReasoning
 from .neurosymbolic import NeurosymbolicReasoning
 from .emergent import EmergentReasoning
-from .meta import MetaLearningStrategy
+from .meta_learning import MetaLearningStrategy
 from .chain_of_thought import ChainOfThoughtStrategy
 from .tree_of_thoughts import TreeOfThoughtsStrategy
 from .recursive import RecursiveReasoning
 from .analogical import AnalogicalReasoning
-from .causal import CausalReasoning
-from .state_space import StateSpaceSearch
-from .counterfactual import CounterfactualReasoning
-from .meta_reasoning import MetaReasoning
-from .engine import BavePantherReasoning
+from .specialized import SpecializedReasoning
+from .local_llm import LocalLLMStrategy
+from .market_analysis import MarketAnalysisStrategy
+from .portfolio_optimization import PortfolioOptimizationStrategy
+from .venture_strategies import VentureStrategy
+from .monetization import MonetizationStrategy
+from .unified_engine import UnifiedReasoningEngine
 
 __all__ = [
     'ReasoningStrategy',
     'MultiModalReasoning',
     'BayesianReasoning',
     'QuantumReasoning',
-    'QuantumInspiredStrategy',
     'NeurosymbolicReasoning',
     'EmergentReasoning',
     'MetaLearningStrategy',
@@ -62,9 +57,11 @@ __all__ = [
     'TreeOfThoughtsStrategy',
     'RecursiveReasoning',
     'AnalogicalReasoning',
-    'CausalReasoning',
-    'StateSpaceSearch',
-    'CounterfactualReasoning',
-    'MetaReasoning',
-    'BavePantherReasoning'
+    'SpecializedReasoning',
+    'LocalLLMStrategy',
+    'MarketAnalysisStrategy',
+    'PortfolioOptimizationStrategy',
+    'VentureStrategy',
+    'MonetizationStrategy',
+    'UnifiedReasoningEngine'
 ]

reasoning/emergent.py

@@ -0,0 +1,115 @@
+"""
+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 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