Implement distributed network of agentic cognitive grammar for OpenCoq/echo9ml

[Copilot]

This PR implements a comprehensive distributed network of agentic cognitive grammar system as specified in the issue. The implementation provides all seven core components through a modular, extensible architecture that integrates seamlessly with existing Echo9ML components.

🧠 Components Implemented

1. Distributed Agentic Kernel (Echo9ML Node)

• distributed_cognitive_grammar.py - Core distributed agent system
• DistributedCognitiveAgent base class with async message processing
• Echo9MLNode specialized agent for Echo9ML functionality
• DistributedCognitiveNetwork coordinator with peer discovery and health monitoring

2. Hypergraph Representation (AtomSpace Integration)

• Extends existing memory_management.py hypergraph system
• HypergraphFragment for serializable knowledge exchange between agents
• Semantic weight-based knowledge sharing with attention filtering

3. GGML Tensor Kernel (Custom Shapes)

• ggml_tensor_kernel.py - Custom tensor operations for cognitive processing
• Prime factorization tensor shapes for evolutionary flexibility:

  "persona": (3, 7, 13, 5, 2)    # 2,730 elements
  "memory": (101, 8, 5, 7, 3)    # 84,840 elements
  "attention": (17, 17, 11, 7, 2) # 44,506 elements

• Custom operations: PERSONA_EVOLVE, ATTENTION_SPREAD, MEMORY_CONSOLIDATE, REASONING_PROPAGATE, LEARNING_ADAPT

4. Communication Substrate (Async Messaging/IPC)

• Extends swarmprotocol.py with cognitive message types
• CognitiveMessage structured format with priority and response handling
• 8 message types: HYPERGRAPH_FRAGMENT, TENSOR_UPDATE, ATTENTION_ALLOCATION, etc.

5. Attention Allocation (ECAN-inspired Module)

• Integrates with echoself_introspection.py attention allocator
• Economic attention allocation across distributed hypergraph networks
• Dynamic threshold adjustment based on cognitive load and activity

6. Symbolic Reasoning (PLN/Pattern Matcher)

• symbolic_reasoning.py - Probabilistic Logic Networks inspired reasoning
• SymbolicAtomSpace with truth value system (strength/confidence)
• Forward/backward chaining inference with pattern matching
• Built-in rules: inheritance transitivity, similarity symmetry

7. Adaptive Learning (MOSES Evolutionary Search)

• Evolutionary tensor operations with learning rate modulation
• Rule adaptation based on inference success rates
• Network topology evolution through dynamic peer discovery

🚀 Usage Example

from distributed_cognitive_grammar import DistributedCognitiveNetwork, Echo9MLNode

# Create network with specialized agents
network = DistributedCognitiveNetwork()
creative_agent = Echo9MLNode("creative_agent", network.broker)
logical_agent = Echo9MLNode("logical_agent", network.broker)

network.add_agent(creative_agent)
network.add_agent(logical_agent)

# Start distributed cognitive processing
await network.start_network()

🧪 Testing & Validation

• test_distributed_cognitive_grammar.py - Comprehensive integration tests
• demo_distributed_cognitive_grammar.py - Interactive demonstration with specialized agents
• All 7 requirements validated ✅
• Full integration with existing Echo9ML components
• Mock implementations handle missing dependencies gracefully

📚 Documentation

• DISTRIBUTED_COGNITIVE_GRAMMAR.md - Complete architecture documentation
• Usage examples, configuration guides, and performance considerations
• Integration patterns with existing components
• Security and monitoring guidelines

🔗 Integration Points

The implementation follows the "minimal changes" principle by:

• Extending existing swarmprotocol.py rather than replacing it
• Integrating with memory_management.py hypergraph system
• Using echoself_introspection.py attention allocation
• Maintaining compatibility with all existing Echo9ML modules

🎯 Implementation Results

• ✅ Distributed Agentic Kernel - Autonomous cognitive agents with async processing
• ✅ Hypergraph Representation - Knowledge fragments with semantic weights
• ✅ GGML Tensor Kernel - Custom shapes with prime factorization
• ✅ Communication Substrate - Async messaging with pub/sub patterns
• ✅ Attention Allocation - ECAN-inspired resource allocation
• ✅ Symbolic Reasoning - PLN pattern matching and inference
• ✅ Adaptive Learning - Evolutionary tensor operations and rule adaptation

The system successfully demonstrates recursive, adaptive cognitive grammar evolution across distributed agent networks while maintaining the Deep Tree Echo architectural principles.

Fixes #1.

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[drzo]

exceptional