NSCR planning: NeuralNetworkEngine and IntegrationEngine

[TheTekton337]

Integration Layer Plan for NSCR Framework

The NSCR (Neuro-Symbolic Cognitive Reasoning) framework involves integrating the SymbolicReasoningEngine with a neural network engine and an integration engine.

Plan Summary:

The NSCR framework aims to seamlessly integrate symbolic reasoning with neural network capabilities through the Integration Engine. By defining common data representations, protocols, and feedback mechanisms, this framework will allow for a combination of symbolic and neural reasoning to tackle complex cognitive tasks.

Components:

1. SymbolicReasoningEngine (SRE):

   • This is the core reasoning engine based on symbolic reasoning.
   • It handles logical rules, facts, and inference.

2. Neural Network Engine (NNE):

   • Responsible for training and executing neural networks.
   • Deals with pattern recognition, learning from data, and complex non-linear relationships.

3. Integration Engine (IE):

   • Serves as the middleware that connects SRE and NNE.
   • Manages communication and data flow between symbolic reasoning and neural network processing.

Plan:

1. Define a Common Data Representation:

• Establish a common format for representing facts, rules, and inputs/outputs that both SRE and NNE can understand.
• This could be a shared data structure or a standard data interchange format.

2. Data Exchange Protocols:

• Define protocols for exchanging data between SRE, NNE, and IE.
• Consider using standard formats like JSON or Protocol Buffers for seamless communication.

3. SRE Changes:

• Modify SRE to expose interfaces for:
  • Accepting input data in the common format.
  • Retrieving output in the common format.
• Enhance SRE to work with continuous data flows, as neural networks often require iterative training.

4. IE Implementation:

• Create the Integration Engine (IE) to facilitate communication between SRE and NNE.
• IE should handle data preprocessing, passing data between SRE and NNE, and handling feedback loops.

5. Integration Logic:

• Implement logic within IE to decide when to involve the neural network engine based on certain conditions.
• Define rules for deciding whether to delegate to the neural network or rely solely on symbolic reasoning.

6. Feedback Mechanism:

• Establish a feedback mechanism where the output from NNE influences the symbolic reasoning in SRE and vice versa.
• Ensure that learning from data is integrated into the overall reasoning process.

7. Testing and Validation:

• Rigorous testing is crucial to ensure the seamless integration of SRE, NNE, and IE.
• Implement test cases that cover various scenarios, including cases where neural network results affect symbolic reasoning and vice versa.

8. Documentation:

• Provide clear documentation for developers and users explaining how to use the integrated NSCR framework.
• Include guidelines on configuring, training, and maintaining the system.

9. Example Use Cases:

• Create example use cases or applications that showcase the synergies between symbolic reasoning and neural networks in the NSCR framework.
• Demonstrate how the combined capabilities of SRE and NNE lead to more powerful and flexible reasoning.

[TheTekton337]

Define Integration Layer Interfaces

1. SymbolicReasoningEngine Interface:

   • Define an interface or set of traits in the SymbolicReasoningEngine package that the Integration Layer can implement.
   • Include methods for exchanging information between the SymbolicReasoningEngine and the Neural Network Engine, such as input and output data formats.

2. Neural Network Engine Interface:

   • Similarly, define an interface or set of traits in the Neural Network Engine package that the Integration Layer can implement.
   • Include methods for training, inference, and exchanging data with the SymbolicReasoningEngine.

Example Trait for SymbolicReasoningEngine:

trait SymbolicIntegration {
    fn send_symbolic_data(&self, data: &SymbolicData);
    fn receive_neural_output(&self, output: &NeuralOutput);
}

Example Trait for Neural Network Engine:

trait NeuralIntegration {
    fn send_neural_data(&self, data: &NeuralData);
    fn receive_symbolic_output(&self, output: &SymbolicOutput);
}

Update SymbolicReasoningEngine for Integration

1. Modify SymbolicReasoningEngine:
   • Add methods or components that allow it to send data to the Integration Layer.
   • Enhance the engine's capability to receive and process output from the Integration Layer.

Example Implementation in SymbolicReasoningEngine:

struct SymbolicReasoningEngine {
    // ... existing fields

    // Integration Layer reference
    integration_layer: Box<dyn SymbolicIntegration>,
}

impl SymbolicReasoningEngine {
    pub fn new(integration_layer: Box<dyn SymbolicIntegration>) -> Self {
        SymbolicReasoningEngine {
            // ... initialize other fields
            integration_layer,
        }
    }

    pub fn process_neural_output(&self, output: &NeuralOutput) {
        // Process neural network output and update the reasoning engine state
        // ...
    }
}

Implement Integration Layer

1. Create Integration Layer Package:

   • Develop a new package, e.g., IntegrationLayer, responsible for implementing the interfaces defined in Step 1.
   • This package acts as the bridge between the SymbolicReasoningEngine and the Neural Network Engine.

2. Implement Integration Traits:

   • In the IntegrationLayer package, implement the traits defined for integration with both the SymbolicReasoningEngine and the Neural Network Engine.

Example Implementation in IntegrationLayer Package:

struct IntegrationLayer;

impl SymbolicIntegration for IntegrationLayer {
    fn send_symbolic_data(&self, data: &SymbolicData) {
        // Send symbolic data to the Neural Network Engine
        // ...
    }

    fn receive_neural_output(&self, output: &NeuralOutput) {
        // Receive neural network output and forward it to the SymbolicReasoningEngine
        // ...
    }
}

impl NeuralIntegration for IntegrationLayer {
    fn send_neural_data(&self, data: &NeuralData) {
        // Send neural data to the SymbolicReasoningEngine
        // ...
    }

    fn receive_symbolic_output(&self, output: &SymbolicOutput) {
        // Receive symbolic output and forward it to the Neural Network Engine
        // ...
    }
}

Create Neural Network Engine

1. Neural Network Engine:
   • Neural Network Engine package to include the defined integration traits.
   • Implement the methods for sending and receiving data.

Example NeuralNetworkEngine Package:

struct NeuralNetworkEngine {
    // ... other fields

    // Integration Layer reference
    integration_layer: Box<dyn NeuralIntegration>,
}

impl NeuralNetworkEngine {
    pub fn new(integration_layer: Box<dyn NeuralIntegration>) -> Self {
        NeuralNetworkEngine {
            // ... initialize other fields
            integration_layer,
        }
    }

    pub fn process_symbolic_output(&self, output: &SymbolicOutput) {
        // Process symbolic output and update the neural network engine state
        // ...
    }
}

Create NSCR Framework Package

1. NSCR Framework Package:
   • Assemble the three packages (SymbolicReasoningEngine, NeuralNetworkEngine, and IntegrationLayer) into an NSCR framework package.
   • Define a common API for users to interact with both symbolic reasoning and neural network components.

Example NSCR Framework Package:

struct NSCRFramework {
    symbolic_reasoning_engine: SymbolicReasoningEngine,
    neural_network_engine: NeuralNetworkEngine,
    integration_layer: IntegrationLayer,
}

impl NSCRFramework {
    pub fn new() -> Self {
        // Initialize instances of SymbolicReasoningEngine, NeuralNetworkEngine, and IntegrationLayer
        let integration_layer = Box

::new(IntegrationLayer);
        let symbolic_reasoning_engine = SymbolicReasoningEngine::new(integration_layer.clone());
        let neural_network_engine = NeuralNetworkEngine::new(integration_layer);

        NSCRFramework {
            symbolic_reasoning_engine,
            neural_network_engine,
            integration_layer,
        }
    }
}