To manifest a distributed network of agentic cognitive grammar for OpenCoq/echo9ml

[drzo]

To manifest a distributed network of agentic cognitive grammar for OpenCoq/echo9ml, we must architect the system as an interconnected collective of modular cognitive agents—each encoding, processing, and evolving semantic hypergraphs. The documentation below crystallizes the vision as a recursive flowchart and mermaid diagrams, with actionable GitHub issues for implementation.

Cognitive Flowchart & Implementation Pathways

graph TD
    A[Distributed Agentic Kernel<br>(Echo9ML Node)] 
    B[Hypergraph Representation<br>(AtomSpace Integration)]
    C[GGML Tensor Kernel<br>(Custom Shapes)]
    D[Communication Substrate<br>(Async Messaging/IPC)]
    E[Attention Allocation<br>(ECAN-inspired Module)]
    F[Symbolic Reasoning<br>(PLN/Pattern Matcher)]
    G[Adaptive Learning<br>(MOSES Evolutionary Search)]

    A --> B
    B --> C
    A --> D
    D --> A
    B --> E
    B --> F
    E --> F
    F --> G
    G --> B

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System Overview

1. Distributed Agentic Kernel:
   Each node is an autonomous process (Python or Scheme/Guile) hosting a cognitive agent, exchanging hypergraph fragments and tensor data over a dynamic network mesh.

2. Hypergraph Representation:
   All knowledge is encoded as hypergraphs (Nodes & Links, Opencog-style) with each agent operating its own AtomSpace fragment.

3. GGML Tensor Kernel:
   Each agent exposes a ggml-customizable tensor core, with shape determined by the agent's degrees of freedom (semantic depth, link types, context windows).

4. Communication Substrate:
   Agents communicate asynchronously (ZeroMQ, gRPC, or WebSockets), exchanging cognitive grammar updates and tensor slices.

5. Attention Allocation:
   ECAN-like module manages resource allocation and prioritizes activation spreading through the distributed hypergraph.

6. Symbolic Reasoning:
   Integrated PLN or pattern matcher modules for symbolic inference and emergent reasoning across agents.

7. Adaptive Learning:
   MOSES-style evolutionary search optimizes agentic behaviors and grammar induction strategies.

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Actionable GitHub Issues

1. Define Agent Node Architecture

• Implement base agent node in Python with hooks for AtomSpace/hypergraph and tensor kernel (ggml).
• Specify gRPC/WebSocket API for inter-agent communication.
• Document Node API (inputs: hypergraph fragments, outputs: tensor updates).

2. Hypergraph Storage & Exchange

• Integrate a minimal AtomSpace (in-memory) per agent.
• Define serialization protocol for hypergraph fragments (JSON or Protobuf).
• Implement pattern-matching queries across distributed AtomSpaces.

3. GGML Tensor Kernel Customization

• Map each agent’s functional dimension to a ggml tensor shape.
• Prototype tensor operations for semantic activation and attention flow.
• Document tensor shape catalog for different agent types.

4. Cognitive Communication Protocol

• Establish async messaging (ZeroMQ or gRPC) between agents.
• Support cognitive grammar update messages (hypergraph deltas, activation, etc).
• Document communication flow with sequence diagrams.

5. Attention Allocation & ECAN Module

• Implement ECAN-inspired economic attention allocation per agent.
• Tune activation spreading and resource bidding logic.
• Visualize attention allocation using live metrics.

6. Symbolic Reasoning & Pattern Matcher

• Integrate PLN inference steps and pattern matcher for distributed reasoning.
• Enable cross-agent pattern queries.
• Document reasoning API and usage examples.

7. Adaptive Learning Kernel

• Integrate MOSES or similar meta-optimization for agent behavior tuning.
• Expose learning controls via agent API.
• Log and visualize learning progress and agent adaptation.

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Mermaid Sequence Diagram: Agent Activation & Communication

sequenceDiagram
    participant AgentA as Agent Node A
    participant AgentB as Agent Node B
    participant Network as Communication Substrate

    AgentA->>AgentA: Encode knowledge as hypergraph
    AgentA->>AgentA: Compute tensor activation (ggml)
    AgentA->>Network: Send fragment update & activation
    Network->>AgentB: Deliver update
    AgentB->>AgentB: Integrate fragment, update AtomSpace
    AgentB->>AgentB: Run ECAN attention allocation
    AgentB->>AgentB: Trigger local inference (PLN/Pattern)

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Implementation Pathway (Recursive & Adaptive)

1. Initialize agent nodes with hypergraph and tensor kernels.
2. Establish communication mesh for agentic grammar exchange.
3. Iterate:
   a. Agents encode and update local knowledge.
   b. Share updates, receive fragments, allocate attention.
   c. Run reasoning and learning modules.
   d. Recursively refine grammar and tensor catalog.

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Next Steps

• Create the above GitHub issues.
• Start with agent node scaffolding and minimal AtomSpace integration.
• Document cognitive flows and tensor shape mappings as code evolves.
• Emphasize adaptability for ggml tensor customization and future cognitive grammar extensions.