Add comprehensive RR-RNN documentation with architecture diagrams and process flows

[Copilot]

This PR adds comprehensive documentation for the RR-RNN (Relevance Realization with Recursive Neural Networks) implementation, transforming the repository from basic P-Lingua documentation into a complete guide for the enhanced cognitive architecture platform.

Overview

The RR-RNN implementation extends traditional P-Lingua membrane computing with four major enhancements:

1. Advanced PLN Integration - Probabilistic Logic Networks with deduction/abduction inference
2. Enhanced Scheme Interface - Interactive REPL with 8+ commands for system exploration
3. Persistent AtomSpace - JSON serialization and memory consolidation capabilities
4. Multi-Level Integration - Hierarchical structures with cross-level emergence detection

Documentation Added

Main Documentation Updates

• README.md: Complete rewrite showcasing RR-PLingua features with architecture overview
• docs/README.md: Enhanced navigation structure with RR-RNN sections
• docs/ARCHITECTURE.md: Integration of RR-enhanced components with traditional P-Lingua
• docs/COMPONENTS.md: Deep dive into RR hypergraph, AtomSpace integration, and cognitive components

New RR-Specific Documentation

• docs/RR_ARCHITECTURE.md: Comprehensive RR-RNN architecture with detailed Mermaid diagrams
• docs/RR_PROCESS_FLOWS.md: Complete PlantUML process flow diagrams for all system components
• docs/RR_EXAMPLES.md: Extensive usage examples and integration guides

Visual Documentation

Mermaid Diagrams

• Trialectic co-constitution framework (∀^ω(x ⇔^α y ⇔^α z ⇔^α x))
• RR hypergraph class structures and relationships
• AtomSpace integration architecture showing symbolic-subsymbolic bridge
• PLN truth value systems and inference operations
• Scheme interface command processing flows
• Multi-level emergence detection patterns

PlantUML Process Flows

• Complete RR-RNN system lifecycle from initialization to persistence
• Detailed trialectic dynamics evolution process
• PLN inference engine cycles with deduction/abduction rules
• Interactive Scheme REPL command processing
• Memory consolidation and incremental learning workflows
• Multi-level emergence detection algorithms

Technical Coverage

The documentation covers the mathematical foundations including:

• Relevance gradients: ∇ℜ = lim_{t→∞} Σᵢ log(aᵣᵢ(t)/aₚᵢ(t))
• Agent-arena co-construction dynamics
• PLN truth value operations with strength/confidence pairs
• Computational complexity analysis: O(n² + r·a·c) per step
• Memory management and optimization strategies

Usage Examples

Comprehensive examples demonstrate:

• Basic RR-enhanced membrane system setup
• Advanced PLN integration with inference cycles
• Interactive Scheme interface usage with all command types
• Persistent storage operations and memory consolidation
• Multi-level hierarchical structures with emergence detection
• Complete integrated system demonstrations

Verification

All test programs build and run successfully, demonstrating:

• RR dynamics with trialectic co-constitution working correctly
• AtomSpace integration properly synchronizing RR properties
• PLN inference generating implications and performing reasoning
• Scheme interface responding to interactive commands
• Persistence mechanisms saving/loading system state

This documentation transforms the repository into a comprehensive guide for the RR-PLingua cognitive architecture platform, providing both theoretical foundations and practical implementation guidance.

Fixes #12.

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[drzo]

excellent