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Merge pull request #32 from tylerbessire/claude/hyperon-subagents-integration-011pH5JRaXVGk2jctEGSSHAW
Integrate Hyperon core with subagents
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HYPERON_INTEGRATION_SUMMARY.md

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README.md

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@@ -36,6 +36,22 @@ PUMA is a layered cognitive architecture combining symbolic reasoning (Hyperon/M
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- MeTTa-based reasoning and program execution
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- RocksDB/PostgreSQL persistence layer
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### Hyperon Subagents System
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- **Parallel Distributed Reasoning**: Pool of specialized MeTTa subagents for concurrent execution
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- **5 Core Components**:
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- **SubAgentManager**: Orchestrates agent pool, task routing, and load balancing
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- **MeTTaExecutionEngine**: MeTTa program execution with multiple modes (batch/interactive/async)
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- **SubAgentCoordinator**: Advanced coordination strategies (parallel, sequential, competitive, consensus)
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- **RFTHyperonBridge**: Bridges RFT relational frames with MeTTa symbolic reasoning
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- **HyperonSubAgent**: Individual agents with specialized capabilities (reasoning, pattern matching, memory retrieval, goal planning)
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- **Advanced Capabilities**:
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- Capability-based task routing (8 agent capability types)
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- Map-reduce distributed reasoning
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- Inter-agent communication (broadcast, P2P, pub-sub via Atomspace)
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- Consensus mechanisms for multi-agent validation
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- Integration with PUMA consciousness states
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- Performance monitoring and fault tolerance
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### PUMA Meta-Cognitive Framework
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- **Episodic Memory System**: Timestamped experience nodes with context
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- **Memory Consolidation**: Background pattern extraction and concept formation
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cargo build --release
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cd ..
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# Install Hyperon Python bindings
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pip install hyperon
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# Install GUI dependencies
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cd gui
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npm install
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│ ├── rft/ # Relational Frame Theory engine
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│ ├── curiosity/ # Intrinsic motivation system
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│ ├── goals/ # Goal formation and intention
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│ └── shop/ # Self-modification system
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│ ├── shop/ # Self-modification system
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│ └── hyperon_subagents/ # Hyperon subagent system
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│ ├── manager.py # SubAgent pool management
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│ ├── metta_engine.py # MeTTa execution engine
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│ ├── coordinator.py # Advanced coordination strategies
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│ └── rft_bridge.py # RFT-Hyperon integration bridge
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├── gemini-interface/ # Gemini Live integration
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├── web-agent/ # Autonomous browsing
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├── atomspace-db/ # Persistence layer
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├── gui/ # Real-time visualization dashboard
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├── bootstrap/ # Consciousness initialization
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├── examples/ # Usage examples and demos
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└── tests/ # Integration and unit tests
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```
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consciousness.resume()
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```
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### Using Hyperon Subagents
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```python
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from puma.hyperon_subagents import SubAgentManager, SubAgentTask, AgentCapability
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# Initialize manager with specialized agent pool
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manager = SubAgentManager(max_agents=10)
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manager.create_specialized_agents()
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# Execute a reasoning task
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task = SubAgentTask(
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task_type="reasoning",
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metta_program="(infer (premise) (rule))",
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priority=0.8
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)
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result = await manager.execute_task(task, required_capability=AgentCapability.REASONING)
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# Parallel execution across multiple agents
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tasks = [create_task(i) for i in range(10)]
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results = await manager.execute_parallel(tasks)
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# Map-reduce distributed reasoning
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map_programs = ["(match &self (pattern1 $x) $x)", "(match &self (pattern2 $y) $y)"]
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reduce_program = "(synthesize-concept $results)"
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result = await manager.map_reduce_reasoning(map_programs, reduce_program)
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```
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See `/home/user/PUMA-Program-Understanding-Meta-learning-Architecture/examples/hyperon_subagents_demo.py` for complete examples.
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### GUI Dashboard
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```bash
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cd gui
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- [x] Basic project structure
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- [x] RFT framework foundation
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- [x] Frequency ledger tracking system
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- [x] Hyperon subagents integration
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- [x] SubAgentManager with pool management
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- [x] MeTTa execution engine (batch/interactive/async modes)
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- [x] SubAgentCoordinator with 6 coordination strategies
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- [x] RFT-Hyperon bridge for symbolic reasoning
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- [x] Inter-agent communication (broadcast/P2P/pub-sub)
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- [x] Capability-based task routing
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- [x] Map-reduce distributed reasoning
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- [x] Consensus mechanisms
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### In Progress
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- [ ] Hyperon Atomspace integration
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- [ ] Hyperon Atomspace persistence integration
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- [ ] Bootstrap consciousness seed
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- [ ] Gemini Live interface
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- [ ] Web agent
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- [ ] Memory consolidation
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- [ ] Memory consolidation with Hyperon
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- [ ] Self-modification system
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- [ ] GUI dashboard
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- [ ] GUI dashboard with subagent visualization
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### Planned
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- [ ] Full autonomous operation
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}
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```
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## Documentation
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For detailed documentation on specific components:
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- **Hyperon Subagents System**: See `/home/user/PUMA-Program-Understanding-Meta-learning-Architecture/docs/HYPERON_SUBAGENTS.md`
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- **RFT Architecture**: See `/home/user/PUMA-Program-Understanding-Meta-learning-Architecture/docs/functional_contextualist_architecture.md`
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- **Architecture Overview**: See `/home/user/PUMA-Program-Understanding-Meta-learning-Architecture/docs/architecture.md`
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## References
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- OpenCog Hyperon: https://github.com/trueagi-io/hyperon-experimental

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