Add AGI module: sofia_reasoning.py

sofia_reasoning.py

@@ -0,0 +1,742 @@
+#!/usr/bin/env python3
+"""
+SOFIA Advanced Reasoning Engine
+Implements task decomposition, strategy selection, and logical reasoning
+"""
+
+import re
+import json
+import logging
+from typing import Dict, List, Tuple, Optional, Any, Set
+from datetime import datetime
+from collections import defaultdict, deque
+import heapq
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class Task:
+    """
+    Represents a task that can be decomposed and reasoned about
+    """
+
+    def __init__(self, description: str, complexity: int = 1, dependencies: List[str] = None):
+        self.description = description
+        self.complexity = complexity  # 1-10 scale
+        self.dependencies = dependencies or []
+        self.subtasks = []
+        self.completed = False
+        self.created_at = datetime.now()
+        self.estimated_time = self._estimate_time()
+
+    def _estimate_time(self) -> float:
+        """Estimate time required based on complexity and description"""
+        base_time = self.complexity * 5  # 5 minutes per complexity unit
+
+        # Adjust based on keywords
+        desc_lower = self.description.lower()
+        if any(word in desc_lower for word in ['research', 'analyze', 'investigate']):
+            base_time *= 1.5
+        if any(word in desc_lower for word in ['create', 'build', 'implement']):
+            base_time *= 2.0
+        if any(word in desc_lower for word in ['simple', 'quick', 'basic']):
+            base_time *= 0.5
+
+        return base_time
+
+    def add_subtask(self, subtask: 'Task'):
+        """Add a subtask to this task"""
+        self.subtasks.append(subtask)
+
+    def mark_completed(self):
+        """Mark this task as completed"""
+        self.completed = True
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert task to dictionary representation"""
+        return {
+            'description': self.description,
+            'complexity': self.complexity,
+            'dependencies': self.dependencies,
+            'subtasks': [st.to_dict() for st in self.subtasks],
+            'completed': self.completed,
+            'estimated_time': self.estimated_time,
+            'created_at': self.created_at.isoformat()
+        }
+
+class ReasoningStrategy:
+    """
+    Represents a reasoning strategy for solving problems
+    """
+
+    def __init__(self, name: str, description: str, applicable_domains: List[str],
+                 success_rate: float = 0.7, avg_time: float = 10.0):
+        self.name = name
+        self.description = description
+        self.applicable_domains = applicable_domains
+        self.success_rate = success_rate
+        self.avg_time = avg_time
+        self.usage_count = 0
+        self.success_count = 0
+
+    def is_applicable(self, problem_domain: str, problem_complexity: int) -> bool:
+        """Check if this strategy is applicable to the given problem"""
+        domain_match = problem_domain in self.applicable_domains or 'general' in self.applicable_domains
+
+        # Some strategies work better for different complexity levels
+        if 'simple_problems' in self.applicable_domains and problem_complexity <= 3:
+            return True
+        if 'complex_problems' in self.applicable_domains and problem_complexity >= 7:
+            return True
+
+        return domain_match
+
+    def record_usage(self, success: bool):
+        """Record usage of this strategy"""
+        self.usage_count += 1
+        if success:
+            self.success_count += 1
+        self.success_rate = self.success_count / self.usage_count
+
+    def get_effectiveness_score(self) -> float:
+        """Get effectiveness score for strategy selection"""
+        # Combine success rate with usage experience
+        experience_factor = min(1.0, self.usage_count / 10)  # Diminishing returns after 10 uses
+        return self.success_rate * (0.5 + 0.5 * experience_factor)
+
+class TaskDecomposer:
+    """
+    Decomposes complex tasks into manageable subtasks
+    """
+
+    def __init__(self):
+        self.decomposition_patterns = self._load_patterns()
+
+    def _load_patterns(self) -> Dict[str, List[str]]:
+        """Load task decomposition patterns"""
+        return {
+            'research': [
+                'Define research question',
+                'Identify information sources',
+                'Gather relevant data',
+                'Analyze findings',
+                'Synthesize conclusions'
+            ],
+            'implementation': [
+                'Analyze requirements',
+                'Design solution architecture',
+                'Implement core functionality',
+                'Add error handling',
+                'Test implementation',
+                'Document solution'
+            ],
+            'problem_solving': [
+                'Understand the problem',
+                'Break down into components',
+                'Identify potential solutions',
+                'Evaluate solution options',
+                'Implement chosen solution',
+                'Verify solution works'
+            ],
+            'learning': [
+                'Assess current knowledge',
+                'Identify learning objectives',
+                'Find learning resources',
+                'Study materials',
+                'Practice concepts',
+                'Assess understanding'
+            ]
+        }
+
+    def decompose_task(self, task_description: str, complexity: int = 5) -> Task:
+        """Decompose a task into subtasks"""
+        # Identify task type
+        task_type = self._classify_task_type(task_description)
+
+        # Create main task
+        main_task = Task(task_description, complexity)
+
+        # Get decomposition pattern
+        if task_type in self.decomposition_patterns:
+            pattern = self.decomposition_patterns[task_type]
+
+            # Adjust pattern based on complexity
+            if complexity <= 3:
+                # Simplify for simple tasks
+                pattern = pattern[:3]
+            elif complexity >= 8:
+                # Expand for complex tasks
+                pattern.extend([
+                    'Review and refine',
+                    'Optimize performance',
+                    'Prepare for deployment'
+                ])
+
+            # Create subtasks
+            for i, subtask_desc in enumerate(pattern):
+                subtask_complexity = max(1, complexity // len(pattern))
+                subtask = Task(subtask_desc, subtask_complexity)
+                main_task.add_subtask(subtask)
+
+        else:
+            # Generic decomposition for unknown task types
+            subtasks = self._generic_decomposition(task_description, complexity)
+            for subtask_desc in subtasks:
+                subtask = Task(subtask_desc, max(1, complexity // len(subtasks)))
+                main_task.add_subtask(subtask)
+
+        return main_task
+
+    def _classify_task_type(self, description: str) -> str:
+        """Classify the type of task"""
+        desc_lower = description.lower()
+
+        if any(word in desc_lower for word in ['research', 'investigate', 'analyze', 'study']):
+            return 'research'
+        elif any(word in desc_lower for word in ['implement', 'build', 'create', 'develop']):
+            return 'implementation'
+        elif any(word in desc_lower for word in ['solve', 'fix', 'resolve', 'answer']):
+            return 'problem_solving'
+        elif any(word in desc_lower for word in ['learn', 'understand', 'master', 'study']):
+            return 'learning'
+        else:
+            return 'general'
+
+    def _generic_decomposition(self, description: str, complexity: int) -> List[str]:
+        """Generic task decomposition when no specific pattern matches"""
+        base_steps = [
+            'Plan the approach',
+            'Gather necessary resources',
+            'Execute the main work',
+            'Review and verify results'
+        ]
+
+        if complexity > 5:
+            base_steps.insert(1, 'Break down into smaller steps')
+            base_steps.insert(-1, 'Test and validate')
+
+        return base_steps
+
+class StrategySelector:
+    """
+    Selects the best reasoning strategy for a given problem
+    """
+
+    def __init__(self):
+        self.strategies = self._initialize_strategies()
+
+    def _initialize_strategies(self) -> List[ReasoningStrategy]:
+        """Initialize available reasoning strategies"""
+        return [
+            ReasoningStrategy(
+                "analytical_reasoning",
+                "Break down problem into components and analyze systematically",
+                ["mathematics", "science", "engineering", "general"],
+                success_rate=0.85,
+                avg_time=12.0
+            ),
+            ReasoningStrategy(
+                "creative_problem_solving",
+                "Use creative thinking and brainstorming for novel solutions",
+                ["design", "innovation", "art", "general"],
+                success_rate=0.75,
+                avg_time=15.0
+            ),
+            ReasoningStrategy(
+                "deductive_reasoning",
+                "Apply logical deduction from general principles to specific cases",
+                ["logic", "philosophy", "mathematics", "law"],
+                success_rate=0.80,
+                avg_time=10.0
+            ),
+            ReasoningStrategy(
+                "inductive_reasoning",
+                "Draw general conclusions from specific observations",
+                ["science", "research", "data_analysis"],
+                success_rate=0.70,
+                avg_time=18.0
+            ),
+            ReasoningStrategy(
+                "case_based_reasoning",
+                "Solve problems by adapting solutions from similar past cases",
+                ["medicine", "law", "customer_service", "general"],
+                success_rate=0.78,
+                avg_time=8.0
+            ),
+            ReasoningStrategy(
+                "algorithmic_approach",
+                "Apply step-by-step algorithmic procedures",
+                ["programming", "mathematics", "engineering"],
+                success_rate=0.90,
+                avg_time=6.0
+            ),
+            ReasoningStrategy(
+                "intuitive_reasoning",
+                "Rely on intuition and experience for quick solutions",
+                ["simple_problems", "general"],
+                success_rate=0.65,
+                avg_time=3.0
+            )
+        ]
+
+    def select_strategy(self, problem_description: str, problem_complexity: int = 5,
+                       time_constraint: Optional[float] = None) -> ReasoningStrategy:
+        """
+        Select the best reasoning strategy for the given problem
+
+        Args:
+            problem_description: Description of the problem
+            problem_complexity: Complexity level (1-10)
+            time_constraint: Maximum time allowed (minutes)
+
+        Returns:
+            Selected reasoning strategy
+        """
+
+        # Identify problem domain
+        problem_domain = self._identify_domain(problem_description)
+
+        # Filter applicable strategies
+        applicable_strategies = [
+            strategy for strategy in self.strategies
+            if strategy.is_applicable(problem_domain, problem_complexity)
+        ]
+
+        if not applicable_strategies:
+            # Fallback to general strategies
+            applicable_strategies = [
+                strategy for strategy in self.strategies
+                if 'general' in strategy.applicable_domains
+            ]
+
+        # Apply time constraint if specified
+        if time_constraint:
+            applicable_strategies = [
+                strategy for strategy in applicable_strategies
+                if strategy.avg_time <= time_constraint
+            ]
+
+        if not applicable_strategies:
+            # Ultimate fallback
+            return self.strategies[0]
+
+        # Score strategies based on effectiveness and time
+        scored_strategies = []
+        for strategy in applicable_strategies:
+            effectiveness = strategy.get_effectiveness_score()
+
+            # Penalize slow strategies for complex problems under time pressure
+            time_penalty = 0
+            if time_constraint and strategy.avg_time > time_constraint * 0.8:
+                time_penalty = 0.2
+
+            final_score = effectiveness * (1 - time_penalty)
+            scored_strategies.append((final_score, strategy))
+
+        # Select highest scoring strategy
+        scored_strategies.sort(reverse=True)
+        selected_strategy = scored_strategies[0][1]
+
+        logger.info(f"Selected strategy: {selected_strategy.name} (score: {scored_strategies[0][0]:.3f})")
+        return selected_strategy
+
+    def _identify_domain(self, description: str) -> str:
+        """Identify the problem domain from description"""
+        desc_lower = description.lower()
+
+        domain_keywords = {
+            'mathematics': ['math', 'calculate', 'equation', 'algebra', 'geometry'],
+            'programming': ['code', 'program', 'software', 'algorithm', 'debug'],
+            'science': ['experiment', 'hypothesis', 'theory', 'research', 'data'],
+            'engineering': ['design', 'build', 'construct', 'system', 'architecture'],
+            'business': ['profit', 'market', 'customer', 'strategy', 'finance'],
+            'medicine': ['patient', 'diagnosis', 'treatment', 'health', 'medical'],
+            'law': ['legal', 'contract', 'regulation', 'court', 'justice'],
+            'education': ['learn', 'teach', 'student', 'course', 'knowledge']
+        }
+
+        for domain, keywords in domain_keywords.items():
+            if any(keyword in desc_lower for keyword in keywords):
+                return domain
+
+        return 'general'
+
+class LogicalReasoner:
+    """
+    Performs logical reasoning and inference
+    """
+
+    def __init__(self):
+        self.knowledge_base = defaultdict(list)
+        self.inference_rules = self._load_inference_rules()
+
+    def _load_inference_rules(self) -> List[Dict[str, Any]]:
+        """Load basic inference rules"""
+        return [
+            {
+                'name': 'modus_ponens',
+                'description': 'If P implies Q and P is true, then Q is true',
+                'pattern': lambda premises: self._check_modus_ponens(premises)
+            },
+            {
+                'name': 'transitivity',
+                'description': 'If A relates to B and B relates to C, then A relates to C',
+                'pattern': lambda premises: self._check_transitivity(premises)
+            },
+            {
+                'name': 'contradiction_detection',
+                'description': 'Detect logical contradictions',
+                'pattern': lambda premises: self._check_contradiction(premises)
+            }
+        ]
+
+    def add_knowledge(self, fact: str, category: str = 'general'):
+        """Add a fact to the knowledge base"""
+        self.knowledge_base[category].append({
+            'fact': fact,
+            'added_at': datetime.now(),
+            'confidence': 1.0
+        })
+
+    def draw_inference(self, premises: List[str]) -> Dict[str, Any]:
+        """Draw logical inferences from premises"""
+        inferences = []
+
+        # Apply inference rules
+        for rule in self.inference_rules:
+            result = rule['pattern'](premises)
+            if result:
+                inferences.append({
+                    'rule': rule['name'],
+                    'conclusion': result,
+                    'confidence': 0.8  # Base confidence
+                })
+
+        # Look for patterns in knowledge base
+        kb_inferences = self._knowledge_base_inference(premises)
+        inferences.extend(kb_inferences)
+
+        return {
+            'inferences': inferences,
+            'premises_used': len(premises),
+            'total_inferences': len(inferences)
+        }
+
+    def _check_modus_ponens(self, premises: List[str]) -> Optional[str]:
+        """Check for modus ponens pattern: If P then Q, P -> Q"""
+        # Simplified implementation
+        for premise in premises:
+            if 'if' in premise.lower() and 'then' in premise.lower():
+                # This is a conditional premise
+                parts = re.split(r'\s+(?:if|then)\s+', premise, flags=re.IGNORECASE)
+                if len(parts) >= 2:
+                    condition = parts[0].strip()
+                    conclusion = parts[1].strip()
+
+                    # Check if condition is in other premises
+                    for other_premise in premises:
+                        if other_premise != premise and condition.lower() in other_premise.lower():
+                            return conclusion
+
+        return None
+
+    def _check_transitivity(self, premises: List[str]) -> Optional[str]:
+        """Check for transitivity pattern"""
+        # Simplified implementation - look for chains
+        relationships = []
+        for premise in premises:
+            # Look for patterns like "A is related to B"
+            match = re.search(r'(.+?)\s+(?:is|are|relates? to)\s+(.+)', premise, re.IGNORECASE)
+            if match:
+                relationships.append((match.group(1).strip(), match.group(2).strip()))
+
+        # Check for transitive chains
+        for i, (a1, b1) in enumerate(relationships):
+            for j, (a2, b2) in enumerate(relationships):
+                if i != j and b1.lower() == a2.lower():
+                    return f"{a1} relates to {b2}"
+
+        return None
+
+    def _check_contradiction(self, premises: List[str]) -> Optional[str]:
+        """Check for contradictions"""
+        statements = []
+        negations = []
+
+        for premise in premises:
+            premise_lower = premise.lower()
+            statements.append(premise_lower)
+
+            # Simple negation detection
+            if premise_lower.startswith(('not ', 'no ', 'never ')):
+                negations.append(premise_lower[4:])
+            elif 'not' in premise_lower:
+                # Split on 'not'
+                parts = premise_lower.split('not', 1)
+                if len(parts) == 2:
+                    negations.append(parts[0].strip() + parts[1].strip())
+
+        # Check for contradictions
+        for stmt in statements:
+            for neg in negations:
+                if self._are_contradictory(stmt, neg):
+                    return f"Contradiction detected: '{stmt}' vs 'not {neg}'"
+
+        return None
+
+    def _are_contradictory(self, stmt1: str, stmt2: str) -> bool:
+        """Check if two statements are contradictory"""
+        # Very simplified contradiction detection
+        return stmt1.strip() == stmt2.strip()
+
+    def _knowledge_base_inference(self, premises: List[str]) -> List[Dict[str, Any]]:
+        """Draw inferences using knowledge base"""
+        inferences = []
+
+        for category, facts in self.knowledge_base.items():
+            for fact in facts:
+                fact_text = fact['fact']
+
+                # Check if any premise relates to this fact
+                for premise in premises:
+                    if self._texts_related(premise, fact_text):
+                        inferences.append({
+                            'rule': 'knowledge_base_similarity',
+                            'conclusion': f"Related knowledge: {fact_text}",
+                            'confidence': fact['confidence'] * 0.7,
+                            'category': category
+                        })
+
+        return inferences
+
+    def _texts_related(self, text1: str, text2: str) -> bool:
+        """Check if two texts are related (simplified)"""
+        words1 = set(text1.lower().split())
+        words2 = set(text2.lower().split())
+
+        # Check for word overlap
+        overlap = len(words1.intersection(words2))
+        return overlap >= 2  # At least 2 words in common
+
+class AdvancedReasoningEngine:
+    """
+    Main advanced reasoning engine combining all reasoning capabilities
+    """
+
+    def __init__(self):
+        self.task_decomposer = TaskDecomposer()
+        self.strategy_selector = StrategySelector()
+        self.logical_reasoner = LogicalReasoner()
+
+        # Reasoning history
+        self.reasoning_history = deque(maxlen=1000)
+
+    def reason_about_task(self, task_description: str,
+                         complexity: int = 5,
+                         time_constraint: Optional[float] = None) -> Dict[str, Any]:
+        """
+        Perform comprehensive reasoning about a task
+
+        Args:
+            task_description: Description of the task
+            complexity: Task complexity (1-10)
+            time_constraint: Time limit in minutes
+
+        Returns:
+            Reasoning results
+        """
+
+        reasoning_start = datetime.now()
+
+        # 1. Decompose the task
+        decomposed_task = self.task_decomposer.decompose_task(task_description, complexity)
+
+        # 2. Select reasoning strategy
+        strategy = self.strategy_selector.select_strategy(
+            task_description, complexity, time_constraint
+        )
+
+        # 3. Perform logical reasoning
+        task_premises = [task_description]
+        if decomposed_task.subtasks:
+            task_premises.extend([st.description for st in decomposed_task.subtasks])
+
+        logical_inferences = self.logical_reasoner.draw_inference(task_premises)
+
+        # 4. Generate execution plan
+        execution_plan = self._generate_execution_plan(decomposed_task, strategy)
+
+        # 5. Assess feasibility
+        feasibility = self._assess_feasibility(decomposed_task, time_constraint)
+
+        reasoning_result = {
+            'task_analysis': {
+                'original_task': task_description,
+                'complexity': complexity,
+                'estimated_total_time': decomposed_task.estimated_time,
+                'subtasks_count': len(decomposed_task.subtasks)
+            },
+            'decomposition': decomposed_task.to_dict(),
+            'selected_strategy': {
+                'name': strategy.name,
+                'description': strategy.description,
+                'expected_success_rate': strategy.success_rate,
+                'estimated_time': strategy.avg_time
+            },
+            'logical_inferences': logical_inferences,
+            'execution_plan': execution_plan,
+            'feasibility_assessment': feasibility,
+            'reasoning_time': (datetime.now() - reasoning_start).total_seconds()
+        }
+
+        # Record in history
+        self.reasoning_history.append({
+            'timestamp': datetime.now().isoformat(),
+            'task': task_description,
+            'result': reasoning_result
+        })
+
+        return reasoning_result
+
+    def _generate_execution_plan(self, task: Task, strategy: ReasoningStrategy) -> List[Dict[str, Any]]:
+        """Generate a step-by-step execution plan"""
+        plan = []
+
+        # Add strategy-specific preparation steps
+        if strategy.name == 'analytical_reasoning':
+            plan.append({
+                'step': 'analysis',
+                'description': 'Analyze task components systematically',
+                'estimated_time': 5.0
+            })
+        elif strategy.name == 'creative_problem_solving':
+            plan.append({
+                'step': 'brainstorming',
+                'description': 'Generate multiple solution approaches',
+                'estimated_time': 10.0
+            })
+
+        # Add task subtasks
+        for i, subtask in enumerate(task.subtasks):
+            plan.append({
+                'step': f'subtask_{i+1}',
+                'description': subtask.description,
+                'estimated_time': subtask.estimated_time,
+                'dependencies': subtask.dependencies
+            })
+
+        # Add verification step
+        plan.append({
+            'step': 'verification',
+            'description': 'Verify solution meets requirements',
+            'estimated_time': 3.0
+        })
+
+        return plan
+
+    def _assess_feasibility(self, task: Task, time_constraint: Optional[float]) -> Dict[str, Any]:
+        """Assess the feasibility of completing the task"""
+        total_estimated_time = task.estimated_time
+
+        if time_constraint and total_estimated_time > time_constraint:
+            return {
+                'feasible': False,
+                'reason': f'Estimated time ({total_estimated_time:.1f} min) exceeds constraint ({time_constraint} min)',
+                'recommendation': 'Break task into smaller parts or extend time limit'
+            }
+
+        # Assess based on complexity and subtasks
+        complexity_score = task.complexity / 10.0
+        subtask_score = len(task.subtasks) / 10.0  # Normalize
+
+        feasibility_score = 1.0 - (complexity_score * 0.4 + subtask_score * 0.3)
+
+        if feasibility_score > 0.7:
+            assessment = 'highly_feasible'
+        elif feasibility_score > 0.4:
+            assessment = 'moderately_feasible'
+        else:
+            assessment = 'challenging'
+
+        return {
+            'feasible': feasibility_score > 0.3,
+            'assessment': assessment,
+            'feasibility_score': feasibility_score,
+            'estimated_time': total_estimated_time
+        }
+
+    def learn_from_experience(self, task_description: str, success: bool, actual_time: float):
+        """Learn from task execution experience"""
+        # Find the reasoning result for this task
+        for record in reversed(self.reasoning_history):
+            if record['task'] == task_description:
+                reasoning_result = record['result']
+                strategy_name = reasoning_result['selected_strategy']['name']
+
+                # Find the strategy and update its performance
+                for strategy in self.strategy_selector.strategies:
+                    if strategy.name == strategy_name:
+                        strategy.record_usage(success)
+
+                        # Update time estimate
+                        old_time = strategy.avg_time
+                        strategy.avg_time = (old_time + actual_time) / 2  # Simple averaging
+
+                        logger.info(f"Updated strategy {strategy_name}: success_rate={strategy.success_rate:.3f}, avg_time={strategy.avg_time:.1f}")
+                        break
+
+                break
+
+    def get_reasoning_statistics(self) -> Dict[str, Any]:
+        """Get statistics about reasoning performance"""
+        if not self.reasoning_history:
+            return {'total_reasoning_sessions': 0}
+
+        total_sessions = len(self.reasoning_history)
+        recent_sessions = list(self.reasoning_history)[-50:]  # Last 50 sessions
+
+        # Calculate average reasoning time
+        reasoning_times = [r['result']['reasoning_time'] for r in recent_sessions]
+        avg_reasoning_time = sum(reasoning_times) / len(reasoning_times)
+
+        # Strategy usage statistics
+        strategy_usage = defaultdict(int)
+        for record in recent_sessions:
+            strategy_name = record['result']['selected_strategy']['name']
+            strategy_usage[strategy_name] += 1
+
+        return {
+            'total_reasoning_sessions': total_sessions,
+            'average_reasoning_time': avg_reasoning_time,
+            'strategy_usage': dict(strategy_usage),
+            'most_used_strategy': max(strategy_usage.items(), key=lambda x: x[1])[0] if strategy_usage else None
+        }
+
+
+# Example usage
+if __name__ == "__main__":
+    print("SOFIA Advanced Reasoning Engine")
+    print("This system provides task decomposition, strategy selection, and logical reasoning")
+
+    # Example usage would be integrated with SOFIA
+    """
+    from sofia_reasoning import AdvancedReasoningEngine
+
+    reasoner = AdvancedReasoningEngine()
+
+    # Reason about a complex task
+    result = reasoner.reason_about_task(
+        "Implement a machine learning model for image classification",
+        complexity=8,
+        time_constraint=120  # 2 hours
+    )
+
+    print("Reasoning result:", result)
+
+    # Learn from execution
+    reasoner.learn_from_experience(
+        "Implement a machine learning model for image classification",
+        success=True,
+        actual_time=95.0
+    )
+    """