############################################################ GnuCash Cognitive Engine README

Advanced Neural-Symbolic Accounting System The current stable series is GnuCashCog 2.x.

🧠 Cognitive Architecture Overview

GnuCashCog transforms traditional double-entry bookkeeping into a distributed cognitive tensor network that learns, adapts, and evolves. This implementation creates a "living grammar of cognition" where financial data flows through specialized neural-symbolic nodes.

graph TD
    subgraph "GnuCashCog Cognitive System"
        A[Traditional Accounting] -->|Cognitive Transformation| B[Neural-Symbolic Engine]
        B --> C[Distributed Tensor Network]
        C --> D[Emergent Financial Intelligence]
        
        subgraph "Core Components"
            E[OpenCog Framework<br/>AtomSpace, PLN, ECAN, MOSES, URE]
            F[Tensor Network<br/>Memory, Task, AI, Autonomy]
            G[Cogfluence Clustering<br/>Pattern Recognition]
        end
        
        B --> E
        B --> F
        B --> G
    end
    
    subgraph "Capabilities"
        H[Adaptive Learning]
        I[Predictive Analytics]
        J[Pattern Discovery]
        K[Uncertainty Handling]
        L[Attention Economics]
    end
    
    D --> H
    D --> I
    D --> J
    D --> K
    D --> L

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🎯 Key Features

🔮 Distributed Tensor Network

• Memory Node: Stores transactions, clusters, and states as tensor data
• Task Node: Orchestrates workflow and triggers clustering operations
• AI Node: Financial pattern recognition with Cogfluence clustering
• Autonomy Node: Self-modification and attention allocation

🧮 Neural-Symbolic Integration

• AtomSpace: Hypergraph knowledge representation
• PLN: Probabilistic Logic Networks for validation
• ECAN: Economic Attention Allocation
• MOSES: Meta-Optimizing Evolutionary Search
• URE: Uncertain Reasoning Engine

🎨 Cogfluence Clustering

• Advanced financial pattern recognition
• Emergent economic insight discovery
• Adaptive clustering algorithms
• Real-time pattern detection

⚡ High-Performance Computing

• ggml Integration: Optimized tensor operations
• Message Passing: Asynchronous inter-node communication
• Attention Economics: Dynamic resource allocation
• Distributed Processing: Scalable cognitive architecture

📚 Table of Contents

• 🧠 Cognitive Architecture Overview
• 🎯 Key Features
• 📖 Documentation
• 🔧 Dependencies
• 🚀 Getting Started
• 🏗️ Building & Installing
• 🖥️ Supported Platforms
• 📥 Downloads
• 🔄 Getting Source with Git
• 👨‍💻 Developing GnuCashCog

📖 Documentation

📋 Core Documentation

• 🧠 Cognitive Accounting Framework - Neural-symbolic accounting system
• 🕸️ Tensor Network Architecture - Distributed cognitive nodes
• 📊 Implementation Report - Technical implementation details

🔗 External Resources

• 🌐 Home Page: https://www.gnucash.org/
• 📚 Wiki: https://wiki.gnucash.org/wiki/GnuCash
• 💾 Downloads: https://www.gnucash.org/download

🎨 System Architecture

graph LR
    subgraph "Input Layer"
        A[Financial Data] --> B[Transactions]
        B --> C[Accounts]
        C --> D[Balances]
    end
    
    subgraph "Cognitive Processing"
        E[AtomSpace<br/>Hypergraph] --> F[PLN<br/>Validation]
        F --> G[ECAN<br/>Attention]
        G --> H[MOSES<br/>Evolution]
        H --> I[URE<br/>Uncertainty]
    end
    
    subgraph "Tensor Network"
        J[Memory Node] --> K[Task Node]
        K --> L[AI Node]
        L --> M[Autonomy Node]
    end
    
    subgraph "Output Layer"
        N[Insights] --> O[Predictions]
        O --> P[Adaptations]
        P --> Q[Intelligence]
    end
    
    D --> E
    I --> J
    M --> N
    Q --> A

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🔧 Dependencies

📦 Core Dependencies

• cmake: Build system (https://cmake.org)
• ninja: Fast build tool (https://ninja-build.org)
• glib: Foundation library for cognitive operations
• libxml2: XML processing for financial data
• SWIG: 2.0.10+ for language bindings (http://www.swig.org)

🧠 Cognitive Architecture Dependencies

• OpenCog: Core cognitive framework components
  • atomspace: Hypergraph knowledge representation
  • cogutil: Cognitive utilities and data structures
  • attention: ECAN attention allocation
  • pln: Probabilistic Logic Networks
  • moses: Meta-optimizing evolutionary search
  • ure: Uncertain reasoning engine
• ggml: High-performance tensor operations (optional)
• Cogfluence: Enhanced clustering algorithms

🔍 Optional Dependencies

• Perl: For online stock/currency price retrieval
• Google Test: For comprehensive testing framework
• Valgrind: Memory debugging and profiling

Installation Notes:

• Run gnc-fq-update as root for latest financial quote modules
• Windows: Use "Install Online Quotes" from Start menu
• macOS: Use "Update Finance Quote" from distribution disk image

Please see README.dependencies for detailed build dependency information.

🚀 Getting Started

🎮 Basic Usage

# Launch GnuCashCog
gnucash [filename]

# With cognitive features enabled
gnucash --enable-cognitive-accounting [filename]

# Run tensor network demo
./tensor-network-demo

# Test cognitive accounting
./test-cognitive-accounting.sh

🔧 Environment Variables

• GNC_BOOTSTRAP_SCM: Location of initial bootstrapping scheme code
• GUILE_LOAD_PATH: Override for GnuCash scheme file loading path
• GNC_MODULE_PATH: Override for GnuCash module loading path
• GNC_DEBUG: Enable debugging output for early startup
• GNC_COGNITIVE_DEBUG: Enable cognitive architecture debugging

📁 Sample Files

• *.gnucash: GnuCash account files (Open File menu)
• *.qif: Quicken Import Format files (Import QIF menu)
• doc/examples/: Sample accounts and transactions
• cognitive-accounting-demo.cpp: Cognitive features demonstration

🌍 Internationalization

GnuCashCog supports multiple languages through message catalogs. The system automatically uses your desktop environment's locale, but this can be overridden.

Override Locale Settings:

• Visit: https://wiki.gnucash.org/wiki/Locale_Settings
• Configure regional financial formatting
• Set cognitive reasoning language preferences

🏗️ Building & Installing

🚀 Quick Start

# Configure with cognitive features
cmake -B build -S . -DCMAKE_BUILD_TYPE=Release \
    -DHAVE_COGNITIVE_ACCOUNTING=ON \
    -DHAVE_GGML=ON \
    -DHAVE_COGFLUENCE_CLUSTERING=ON

# Build with ninja (faster)
cmake --build build --parallel

# Install
cmake --install build

🔧 Build Options

• CMAKE_INSTALL_PREFIX: Runtime installation directory
• CMAKE_BUILD_TYPE: Debug, Release, RelWithDebInfo
• HAVE_COGNITIVE_ACCOUNTING: Enable cognitive features
• HAVE_GGML: Enable tensor operations
• HAVE_COGFLUENCE_CLUSTERING: Enable advanced clustering

📦 Dependencies Installation

# Ubuntu/Debian
sudo apt-get install cmake ninja-build libglib2.0-dev libxml2-dev

# macOS
brew install cmake ninja glib libxml2

# Build OpenCog dependencies
git clone https://github.com/opencog/opencog
cd opencog && mkdir build && cd build
cmake .. && make -j4 && sudo make install

🏭 Advanced Build Configuration

# Enable full cognitive stack
set(HAVE_COGNITIVE_ACCOUNTING ON)
set(HAVE_GGML ON)
set(HAVE_COGFLUENCE_CLUSTERING ON)
set(HAVE_OPENCOG_ATOMSPACE ON)
set(HAVE_OPENCOG_PLN ON)
set(HAVE_OPENCOG_ECAN ON)
set(HAVE_OPENCOG_MOSES ON)
set(HAVE_OPENCOG_URE ON)

🚢 DESTDIR Installation

# Stage installation for packaging
make DESTDIR=/tmp/staging install

# Creates staged directory structure under /tmp/staging
# Useful for creating distribution packages

Note: GNOME libraries are required but a GNOME desktop is not necessary.

Platform-Specific Instructions:

• GNU/Linux: https://wiki.gnucash.org/wiki/Building
• macOS: Set GNOME_PATH and GNOME_LIBCONFIG_PATH if needed
• Windows: Use MSYS2 or WSL for cognitive features

🖥️ Supported Platforms

✅ Fully Supported

• GNU/Linux: x86_64, ARM64, with full cognitive features
• macOS: Intel/Apple Silicon, versions 10.15+
• FreeBSD: x86_64, with OpenCog support
• OpenBSD: x86_64, basic cognitive features

🔧 Cognitive Architecture Support

• OpenCog Integration: Linux, macOS, FreeBSD
• Tensor Operations: Platforms with ggml support
• Distributed Processing: All POSIX-compliant systems
• GPU Acceleration: CUDA/OpenCL capable systems (future)

🎯 Performance Tiers

• Tier 1: Full cognitive features + GPU acceleration
• Tier 2: Complete OpenCog integration + tensor operations
• Tier 3: Basic cognitive features + attention allocation
• Tier 4: Traditional accounting with minimal cognitive support

GnuCashCog can be ported to any platform supporting GTK+ and C++17 with sufficient expertise and effort. For platform-specific issues, join our development mailing list at [email protected].

📥 Downloads

🔗 Official Sources

• Source Code: https://github.com/OzCog/gnucashcog-v2
• Releases: https://github.com/OzCog/gnucashcog-v2/releases
• Original GnuCash: https://www.gnucash.org/download

🐧 Distribution Packages

• Ubuntu/Debian: apt-get install gnucash-cognitive
• Fedora/RHEL: dnf install gnucash-cognitive
• Arch Linux: pacman -S gnucash-cognitive
• macOS: brew install gnucash-cognitive

🏗️ Build from Source

For the latest cognitive features, build from source using the instructions above. Distribution packages may lag behind the latest neural-symbolic innovations.

🔄 Getting Source with Git

🚀 Quick Clone

# Clone the cognitive engine repository
git clone https://github.com/OzCog/gnucashcog-v2.git
cd gnucashcog-v2

# Initialize submodules for cognitive dependencies
git submodule update --init --recursive

🌟 Development Workflow

# Fork on GitHub first, then clone your fork
git clone https://github.com/YOUR_USERNAME/gnucashcog-v2.git
cd gnucashcog-v2

# Add upstream remote
git remote add upstream https://github.com/OzCog/gnucashcog-v2.git

# Create feature branch
git checkout -b feature/cognitive-enhancement

# Make changes, test, commit
git add .
git commit -m "Add: Neural-symbolic feature enhancement"

# Push and create pull request
git push origin feature/cognitive-enhancement

📚 Repository Structure

gnucashcog-v2/
├── libgnucash/engine/          # Core cognitive engine
├── gnucash/                    # UI and reporting
├── doc/                        # Documentation
├── COGNITIVE_ACCOUNTING.md     # Neural-symbolic framework
├── TENSOR_NETWORK_ARCHITECTURE.md  # Distributed processing
├── tensor-network-demo.cpp     # Cognitive demonstrations
└── test-cognitive-accounting.sh # Integration tests

👨‍💻 Developing GnuCashCog

🚀 Getting Started

Before contributing to the cognitive engine, complete these steps:

1. 📚 Read the Development Guide

   • https://wiki.gnucash.org/wiki/Development
   • Cognitive Architecture Framework
   • Tensor Network Documentation

2. 🔍 Review Documentation

   • Doxygen: https://code.gnucash.org/docs/MASTER/
   • Cognitive API: Browse libgnucash/engine/gnc-cognitive-accounting.h
   • Tensor Network API: Browse libgnucash/engine/gnc-tensor-network.h

3. 💬 Join the Community

   • Development mailing list: [email protected]
   • Cognitive architecture discussions: GitHub Issues
   • OpenCog integration: [email protected]

4. 🔨 Build and Test

   # Build cognitive features
   cmake -B build -DHAVE_COGNITIVE_ACCOUNTING=ON
   cmake --build build
   
   # Run comprehensive tests
   make test-cognitive-accounting
   make test-tensor-network
   
   # Run demos
   ./tensor-network-demo
   ./cognitive-accounting-demo

🧠 Cognitive Architecture Development

🔧 Core Components

• AtomSpace Integration: Hypergraph knowledge representation
• PLN Reasoning: Probabilistic logic for validation
• ECAN Attention: Economic attention allocation
• Tensor Networks: Distributed cognitive processing
• Cogfluence Clustering: Advanced pattern recognition

📝 Code Style

• Follow existing OpenCog conventions
• Use meaningful variable names reflecting cognitive concepts
• Document complex neural-symbolic algorithms
• Include comprehensive unit tests

🧪 Testing Requirements

• Unit Tests: Test individual cognitive components
• Integration Tests: Test OpenCog module interactions
• Performance Tests: Validate tensor operation efficiency
• Regression Tests: Ensure cognitive features don't break existing functionality

🔄 Contribution Workflow

📋 Submitting Patches

1. 📖 Read: https://wiki.gnucash.org/wiki/Development#Submitting_Patches
2. 🍴 Fork: Create a GitHub fork
3. 🌿 Branch: Create feature branch from stable
4. 🔨 Develop: Implement cognitive enhancements
5. ✅ Test: Ensure all tests pass
6. 📝 Document: Update relevant documentation
7. 🚀 Submit: Create pull request with detailed description

🎯 Focus Areas

• Neural-Symbolic Integration: Bridging symbolic and neural approaches
• Cognitive Economics: Attention allocation and resource management
• Pattern Recognition: Financial insight discovery
• Distributed Processing: Scalable cognitive architectures
• Uncertainty Reasoning: Handling incomplete financial information

🏆 Advanced Topics

🔬 Research Areas

• Emergent Behavior: Complex system properties arising from simple rules
• Cognitive Architectures: Multi-agent financial reasoning systems
• Hypergraph Patterns: Advanced knowledge representation
• Evolutionary Algorithms: Self-improving financial strategies

🤝 Collaboration

• OpenCog Community: Core cognitive architecture development
• GnuCash Community: Traditional accounting integration
• Academic Partnerships: Research collaboration opportunities
• Industry Integration: Real-world cognitive accounting applications

📊 Performance Considerations

• Memory Management: Efficient tensor allocation
• Attention Economics: Dynamic resource allocation
• Message Passing: Optimized inter-node communication
• Parallel Processing: Multi-core cognitive operations

🔮 Future Directions

• GPU Acceleration: CUDA/OpenCL tensor operations
• Distributed Computing: Multi-node cognitive networks
• Real-time Processing: Streaming financial analysis
• Deep Learning: Neural network integration
• Quantum Computing: Quantum-enhanced reasoning (experimental)

Thank you for contributing to the evolution of cognitive accounting! 🧠✨

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"Every contribution helps transmute classical ledgers into cognitive neural-symbolic tapestries, where financial meaning emerges from the recursive interplay of distributed intelligence."