neuro-symbolic-cognitive-architectures

name

neuro-symbolic-cognitive-architectures

description

Neural-Symbolic Cognitive Architectures combining neural networks with symbolic reasoning for interpretable, robust AI systems. Enables explicit knowledge representation, logical inference, and learning from both data and rules. Applicable to reasoning systems, knowledge-intensive tasks, interpretable AI. Trigger: neuro-symbolic AI, symbolic reasoning neural networks, interpretable reasoning, knowledge representation learning, neural-symbolic integration

Neural-Symbolic Cognitive Architectures for Reasoning and Learning

Description

A framework for building cognitive architectures that integrate neural networks with symbolic reasoning systems. These architectures combine the learning capabilities of neural networks with the explicit reasoning and interpretability of symbolic systems, enabling AI systems that can learn from both data and rules while providing transparent, explainable decisions.

Based on: "Neural-Symbolic Cognitive Architectures for Reasoning and Learning" (arXiv:2603.02762, March 2026)

Core Architecture

Three-Layer Design

class NeuroSymbolicArchitecture:
    """
    Three-layer neural-symbolic cognitive architecture:
    1. Perception Layer: Neural networks for raw input processing
    2. Symbolic Layer: Logic engine for reasoning with symbols
    3. Integration Layer: Bidirectional mapping between neural and symbolic
    """
    
    def __init__(self):
        # Neural perception
        self.perception = NeuralEncoder()
        
        # Symbolic reasoning engine
        self.reasoner = SymbolicReasoner()
        
        # Neural-symbolic interface
        self.neural_to_symbolic = GroundingModule()
        self.symbolic_to_neural = UnGroundingModule()
    
    def forward(self, input_data):
        # 1. Perception: raw input to features
        features = self.perception(input_data)
        
        # 2. Grounding: features to symbols
        symbols = self.neural_to_symbolic(features)
        
        # 3. Symbolic reasoning
        inferred_symbols = self.reasoner(symbols)
        
        # 4. Un-grounding: symbols back to features
        output = self.symbolic_to_neural(inferred_symbols)
        
        return output

Key Components

1. Grounding Module: Maps neural activations to discrete symbols
2. Symbolic Reasoner: Applies logical rules to inferred symbols
3. Un-Grounding Module: Maps symbolic results back to neural representations

Reasoning Mechanisms

Rule-Based Inference

class SymbolicReasoner:
    """
    Symbolic reasoning engine with differentiable logic.
    Supports both hard logical constraints and soft probabilistic inference.
    """
    
    def __init__(self, rules):
        self.rules = rules  # List of logical rules
        self.knowledge_base = KnowledgeGraph()
    
    def infer(self, symbols):
        """Apply logical rules to derive new facts."""
        # Populate knowledge base
        self.knowledge_base.update(symbols)
        
        # Apply inference rules
        derived_facts = []
        for rule in self.rules:
            if rule.matches(self.knowledge_base):
                derived_facts.extend(rule.apply(self.knowledge_base))
        
        return derived_facts

Differentiable Logic Programming

class DifferentiableLogic:
    """
    Logic programming with differentiable semantics.
    Enables end-to-end training through symbolic reasoning.
    """
    
    @staticmethod
    def AND(a, b):
        return torch.min(a, b)
    
    @staticmethod
    def OR(a, b):
        return torch.max(a, b)
    
    @staticmethod
    def NOT(a):
        return 1.0 - a
    
    @staticmethod
    def IMPLIES(a, b):
        return torch.max(1.0 - a, b)

Training Strategies

Joint Neural-Symbolic Training

def train_neuro_symbolic(model, data, rules, epochs=100):
    """
    Train with both data-driven loss and rule-based constraints.
    """
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
    
    for epoch in range(epochs):
        optimizer.zero_grad()
        
        # Data-driven loss
        predictions = model(data.inputs)
        data_loss = F.cross_entropy(predictions, data.labels)
        
        # Symbolic constraint loss
        symbols = model.ground(predictions)
        rule_satisfaction = model.reasoner.check_rules(symbols, rules)
        constraint_loss = 1.0 - rule_satisfaction
        
        # Combined loss
        total_loss = data_loss + lambda_constraint * constraint_loss
        total_loss.backward()
        optimizer.step()

Applications

• Scientific reasoning: Combine empirical data with domain knowledge
• Medical diagnosis: Integrate patient data with medical knowledge bases
• Legal reasoning: Apply legal rules to case facts
• Education: Teach AI systems with both examples and explanations

Benefits

Feature	Pure Neural	Pure Symbolic	Neuro-Symbolic
Learning from data	✓	✗	✓
Explicit reasoning	✗	✓	✓
Interpretability	Low	High	High
Robustness to noise	High	Low	High
Generalization	Statistical	Logical	Both