Implement distributed network of agentic cognitive grammar for OpenCoq/echo9ml

DISTRIBUTED_COGNITIVE_GRAMMAR.md

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+# Distributed Network of Agentic Cognitive Grammar
+
+## Overview
+
+This implementation creates a distributed network of agentic cognitive grammar for OpenCoq/echo9ml as specified in the original issue. The system consists of interconnected cognitive agents that encode, process, and evolve semantic hypergraphs through a combination of symbolic reasoning, tensor operations, and distributed communication.
+
+## Architecture Components
+
+### 1. Distributed Agentic Kernel (Echo9ML Node)
+
+**File:** `distributed_cognitive_grammar.py`
+
+The core component implementing autonomous cognitive agents:
+
+- **DistributedCognitiveAgent**: Base class for all cognitive agents
+- **Echo9MLNode**: Specialized agent for Echo9ML functionality
+- **DistributedCognitiveNetwork**: Network coordinator managing agent interactions
+
+#### Key Features:
+- Asynchronous message processing
+- Peer discovery and network coordination
+- Hypergraph fragment sharing
+- Attention allocation coordination
+- Adaptive heartbeat monitoring
+
+### 2. Hypergraph Representation (AtomSpace Integration)
+
+**Integration with existing:** `memory_management.py`
+
+The system uses hypergraph structures to represent knowledge:
+
+- **HypergraphFragment**: Serializable knowledge fragments for inter-agent sharing
+- **MemoryNode**: Individual knowledge nodes with semantic metadata
+- **Edge relationships**: Connections between knowledge nodes
+
+#### Knowledge Representation:
+```python
+HypergraphFragment(
+    id="fragment_123",
+    nodes=[{"id": "concept_1", "content": "creativity", "salience": 0.9}],
+    edges=[{"from": "concept_1", "to": "concept_2", "type": "similarity", "weight": 0.8}],
+    semantic_weight=0.85,
+    source_agent="agent_1"
+)
+```
+
+### 3. GGML Tensor Kernel (Custom Shapes)
+
+**File:** `ggml_tensor_kernel.py`
+
+Custom tensor operations for cognitive processing:
+
+- **CognitiveTensor**: Semantically meaningful tensors with metadata
+- **TensorOperationType**: Custom operations for cognitive functions
+- **Prime factorization**: Tensor shapes optimized for evolutionary flexibility
+
+#### Tensor Shape Specification:
+```python
+# Persona tensor: [persona_id, trait_id, time, context, valence]
+"persona": (3, 7, 13, 5, 2)  # 3x7x13x5x2 = 2730 elements
+
+# Memory tensor: [memory_node, memory_type, salience, temporal, relational]
+"memory": (101, 8, 5, 7, 3)  # 101x8x5x7x3 = 84,840 elements
+
+# Attention tensor: [source, target, strength, context, decay]
+"attention": (17, 17, 11, 7, 2)  # 17x17x11x7x2 = 44,506 elements
+```
+
+#### Custom Operations:
+- **PERSONA_EVOLVE**: Evolutionary adaptation of persona traits
+- **ATTENTION_SPREAD**: Attention allocation across cognitive networks
+- **MEMORY_CONSOLIDATE**: Memory consolidation based on salience
+- **REASONING_PROPAGATE**: Reasoning pattern propagation
+- **LEARNING_ADAPT**: MOSES-style evolutionary search
+
+### 4. Communication Substrate (Async Messaging/IPC)
+
+**Extension of:** `swarmprotocol.py`
+
+Asynchronous communication system:
+
+- **CognitiveMessage**: Structured message format for inter-agent communication
+- **MessageType**: Different types of cognitive messages
+- **MessageBroker**: Pub/sub messaging infrastructure
+
+#### Message Types:
+- HYPERGRAPH_FRAGMENT: Knowledge sharing
+- TENSOR_UPDATE: Tensor catalog synchronization
+- ATTENTION_ALLOCATION: Attention coordination
+- REASONING_QUERY/RESULT: Distributed reasoning
+- LEARNING_UPDATE: Learning synchronization
+- HEARTBEAT: Network health monitoring
+- DISCOVERY: Peer discovery
+
+### 5. Attention Allocation (ECAN-inspired Module)
+
+**Integration with:** `echoself_introspection.py`
+
+Economic attention allocation system:
+
+- **AdaptiveAttentionAllocator**: Dynamic attention threshold adjustment
+- **Attention spreading**: Propagation through hypergraph networks
+- **Resource bidding**: Economic allocation of cognitive resources
+
+### 6. Symbolic Reasoning (PLN/Pattern Matcher)
+
+**File:** `symbolic_reasoning.py`
+
+Probabilistic Logic Networks inspired reasoning:
+
+- **SymbolicAtomSpace**: Knowledge representation and reasoning
+- **Atom/Link**: Basic knowledge structures with truth values
+- **Pattern**: Template matching for knowledge queries
+- **Rule**: Inference rules for knowledge derivation
+
+#### Truth Value System:
+```python
+TruthValue(strength=0.9, confidence=0.8)
+# strength: how true the statement is (0-1)
+# confidence: how certain we are about the truth value (0-1)
+```
+
+#### Inference Rules:
+- **Inheritance transitivity**: If A inherits from B and B inherits from C, then A inherits from C
+- **Similarity symmetry**: If A is similar to B, then B is similar to A
+- **Forward chaining**: Generate new knowledge from existing facts
+- **Backward chaining**: Find proofs for goals
+
+### 7. Adaptive Learning (MOSES Evolutionary Search)
+
+**Integration points:** Throughout the system
+
+Evolutionary optimization components:
+
+- **Tensor evolution**: Genetic algorithm-like modification of tensor weights
+- **Rule evolution**: Adaptation of inference rules based on success
+- **Attention evolution**: Dynamic adjustment of attention allocation strategies
+- **Network topology evolution**: Adaptive connection patterns between agents
+
+## System Integration
+
+### Agent Lifecycle
+
+1. **Initialization**: 
+   - Create tensor kernel with default shapes
+   - Initialize symbolic atom space with basic patterns/rules
+   - Set up communication channels
+
+2. **Processing Loop**:
+   - Process incoming messages
+   - Update local knowledge and tensors
+   - Perform symbolic reasoning
+   - Share knowledge fragments with peers
+   - Send heartbeat messages
+
+3. **Knowledge Sharing**:
+   - Export high-attention atoms and links
+   - Convert to hypergraph fragments
+   - Broadcast to network
+   - Import fragments from other agents
+
+### Communication Flow
+
+```mermaid
+sequenceDiagram
+    participant AgentA as Agent A
+    participant AgentB as Agent B
+    participant Network as Message Broker
+
+    AgentA->>AgentA: Process cognitive state
+    AgentA->>AgentA: Update tensors (persona_evolve)
+    AgentA->>AgentA: Perform symbolic reasoning
+    AgentA->>Network: Broadcast hypergraph fragment
+    Network->>AgentB: Deliver fragment
+    AgentB->>AgentB: Integrate fragment into atom space
+    AgentB->>AgentB: Update attention allocation
+    AgentB->>AgentB: Trigger local reasoning
+    AgentB->>Network: Query distributed reasoning
+    Network->>AgentA: Deliver reasoning query
+    AgentA->>Network: Send reasoning result
+    Network->>AgentB: Deliver result
+```
+
+## Usage Examples
+
+### Basic Agent Creation
+
+```python
+from distributed_cognitive_grammar import DistributedCognitiveNetwork, Echo9MLNode
+
+# Create network
+network = DistributedCognitiveNetwork()
+
+# Create agents
+agent1 = Echo9MLNode("echo_agent_1", network.broker)
+agent2 = Echo9MLNode("echo_agent_2", network.broker)
+
+# Add to network
+network.add_agent(agent1)
+network.add_agent(agent2)
+
+# Start network
+await network.start_network()
+```
+
+### Tensor Operations
+
+```python
+from ggml_tensor_kernel import GGMLTensorKernel, TensorOperationType
+
+# Create tensor kernel
+kernel = GGMLTensorKernel("agent_1")
+
+# Create persona tensor
+persona_tensor = kernel.create_tensor("persona_state", "persona", "cognitive_traits")
+
+# Execute evolution operation
+success = kernel.execute_operation(
+    TensorOperationType.PERSONA_EVOLVE,
+    ["persona_state"],
+    "persona_evolved",
+    learning_rate=0.1
+)
+```
+
+### Symbolic Reasoning
+
+```python
+from symbolic_reasoning import SymbolicAtomSpace, Atom, Link, TruthValue
+
+# Create atom space
+atom_space = SymbolicAtomSpace("agent_1")
+
+# Add knowledge
+cat = Atom("cat", "ConceptNode", TruthValue(0.9, 0.8))
+animal = Atom("animal", "ConceptNode", TruthValue(0.95, 0.9))
+cat_animal = Link("InheritanceLink", [cat, animal], TruthValue(0.9, 0.8))
+
+atom_space.add_atom(cat)
+atom_space.add_atom(animal)
+atom_space.add_link(cat_animal)
+
+# Perform inference
+new_items = atom_space.forward_chain(max_iterations=5)
+```
+
+## Configuration
+
+### Tensor Shape Customization
+
+Modify tensor shapes in `ggml_tensor_kernel.py`:
+
+```python
+def _initialize_tensor_shapes(self):
+    self.tensor_shapes.update({
+        "custom_tensor": (prime1, prime2, prime3, prime4),
+        # Use prime numbers for evolutionary flexibility
+    })
+```
+
+### Attention Allocation Tuning
+
+Adjust attention parameters in cognitive agents:
+
+```python
+def adaptive_attention(self, current_load: float, recent_activity: float) -> float:
+    threshold = self.base_threshold + (current_load * 0.3) + (0.2 - recent_activity)
+    return max(0.0, min(1.0, threshold))
+```
+
+### Communication Patterns
+
+Customize message handling in `DistributedCognitiveAgent`:
+
+```python
+async def _handle_custom_message(self, message: CognitiveMessage):
+    # Custom message processing logic
+    pass
+```
+
+## Testing
+
+Run the comprehensive test suite:
+
+```bash
+python test_distributed_cognitive_grammar.py
+```
+
+The test demonstrates:
+- Multi-agent network creation
+- Knowledge sharing between agents
+- Tensor operations across the network
+- Symbolic reasoning coordination
+- Attention allocation synchronization
+
+## Integration with Existing Components
+
+The distributed cognitive grammar system integrates seamlessly with existing Echo9ML components:
+
+- **memory_management.py**: Hypergraph knowledge storage
+- **swarmprotocol.py**: Distributed communication substrate
+- **echoself_introspection.py**: Attention allocation mechanisms
+- **echo9ml.py**: Persona encoding and evolution
+- **cognitive_architecture.py**: High-level cognitive coordination
+
+## Performance Considerations
+
+- **Message Batching**: Group related messages to reduce network overhead
+- **Attention Filtering**: Only share high-attention knowledge fragments
+- **Tensor Compression**: Compress tensor data for network transmission
+- **Asynchronous Processing**: Non-blocking message handling for scalability
+
+## Future Extensions
+
+1. **GGML Integration**: Full integration with actual GGML library
+2. **Advanced PLN**: Complete Probabilistic Logic Networks implementation
+3. **Distributed Learning**: Federated learning across agent networks
+4. **Dynamic Topology**: Self-organizing network structures
+5. **Multi-modal Integration**: Vision, audio, and text processing
+6. **Blockchain Consensus**: Distributed agreement mechanisms
+
+## Security Considerations
+
+- **Message Authentication**: Verify message sources
+- **Knowledge Validation**: Validate imported knowledge fragments
+- **Resource Limits**: Prevent resource exhaustion attacks
+- **Network Isolation**: Isolate agent networks as needed
+
+## Monitoring and Diagnostics
+
+The system provides comprehensive monitoring:
+
+- **Network Health**: Active agent count and connectivity
+- **Knowledge Flow**: Fragment sharing statistics
+- **Tensor Evolution**: Tensor modification tracking
+- **Reasoning Performance**: Inference success rates
+- **Attention Allocation**: Resource utilization metrics
+
+## Conclusion
+
+This implementation provides a solid foundation for distributed agentic cognitive grammar in OpenCoq/echo9ml. The modular architecture allows for incremental development and integration with existing systems while maintaining the core principles of distributed cognition, symbolic reasoning, and adaptive learning.
+
+The system successfully demonstrates the key requirements from the original issue:
+- ✅ Distributed Agentic Kernel (Echo9ML Node)
+- ✅ Hypergraph Representation (AtomSpace Integration)
+- ✅ GGML Tensor Kernel (Custom Shapes)
+- ✅ Communication Substrate (Async Messaging/IPC)
+- ✅ Attention Allocation (ECAN-inspired Module)
+- ✅ Symbolic Reasoning (PLN/Pattern Matcher)
+- ✅ Adaptive Learning (MOSES Evolutionary Search)