Implement Next Development Directions: Advanced PLN Integration, Scheme Interface, Persistent AtomSpace, and Multi-Level Integration

[Copilot]

This PR continues the work from issue #9 by implementing all four major Next Development Directions for the Enhanced Relevance Realization (RR) framework with OpenCog AtomSpace integration.

🚀 Key Features Implemented

1. Advanced PLN Integration

• Complete PLN inference engine with deduction and abduction rules
• Automatic implication generation from RR agent-arena coupling patterns
• Truth value propagation using PLN formulas for strength and confidence
• Integrated reasoning cycles that enhance AtomSpace knowledge

// Example: PLN deduction rule application
PLNInferenceEngine pln_engine(atomspace);
pln_engine.performInferenceCycle(rr_hypergraph);
auto results = pln_engine.getInferenceResults();

2. Enhanced Scheme Interface

• Interactive REPL for real-time system exploration and manipulation
• 8+ Scheme commands for querying and modifying RR/AtomSpace state
• Pattern matching and emergent behavior detection
• Live system introspection capabilities

(list-rr-nodes)           ; List all RR nodes with properties
(get-system-relevance)    ; Compute overall system relevance
(run-pln-inference)       ; Execute PLN reasoning cycle
(find-patterns)           ; Detect emergent patterns
(update-salience node-2 0.8) ; Modify node properties

3. Persistent AtomSpace

• JSON serialization for complete AtomSpace and RR hypergraph state
• Incremental learning by merging new experiences with existing knowledge
• Memory consolidation to remove low-confidence atoms
• Cross-session persistence for long-term system evolution

PersistentAtomSpace storage;
storage.saveToFile(atomspace, "system_state.json");
storage.saveRRHypergraph(hypergraph, "rr_dynamics.json");

4. Multi-Level Integration

• Hierarchical membrane structures with nested agent-arena-relation triads
• Cross-level emergence detection spanning multiple hierarchy levels
• Temporal reasoning with trend analysis over time
• Multi-scale dynamics coordination

🔧 Core Improvements

AtomSpace Integration Fixes

• Eliminated duplicate atoms by implementing smart update-or-create logic
• Improved truth value synchronization between RR properties and AtomSpace
• Enhanced memory efficiency with reduced redundant operations

RR Dynamics Enhancements

• Fixed negative divergence using epsilon-based relevance gradient computation
• Better initialization with positive affordance realization values
• Improved trialectic coherence measurement for system stability

📁 New Files Added

• include/pln_integration.hpp - Complete PLN inference engine
• include/scheme_interface.hpp - Interactive Scheme REPL system
• include/persistent_atomspace.hpp - Serialization and persistence layer
• IMPLEMENTATION_SUMMARY.md - Comprehensive documentation
• test_next_directions.cpp - Full feature demonstration
• demo_repl.cpp - Interactive REPL example

🧪 Testing & Validation

All features are comprehensively tested with:

• Basic integration tests validating core RR-AtomSpace interaction
• Feature-specific demos for each Next Development Direction
• Interactive examples showing real-world usage patterns
• Persistence verification with actual file I/O operations

# Run comprehensive test suite
./test_next_directions
# Result: ✓ All 4 Next Development Directions working correctly

# Try interactive Scheme interface
./demo_repl
# Result: Full REPL functionality demonstrated

🎯 Impact

This implementation provides a robust foundation for:

• Advanced cognitive architectures combining symbolic and subsymbolic reasoning
• Interactive system development with real-time exploration capabilities
• Persistent learning systems that accumulate knowledge over time
• Multi-scale emergence studies in membrane computing systems

The enhanced RR framework now successfully bridges dynamic self-organization (RR) with symbolic reasoning (AtomSpace/PLN), representing a significant step toward unified cognitive architectures as outlined in the original INTEGRATION_STRATEGY.md.

Fixes #10.

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[drzo]

Awesome