agent-o-rama

agent-o-rama

Layer 4: Learning and Pattern Extraction for Cognitive Surrogate Systems

Version: 1.1.0 (music-topos enhanced) Trit: +1 (Generator - produces learned patterns) Bundle: learning

Overview

Agent-o-rama trains learning agents on interaction sequences to discover behavioral patterns. It extracts temporal, topic, and network patterns from raw interaction data, producing models compatible with the cognitive-surrogate skill.

Enhanced Integration: Multi-Interpreter

DuckDB Pattern Storage

CREATE TABLE learned_patterns (
    pattern_id VARCHAR PRIMARY KEY,
    pattern_type VARCHAR,  -- 'temporal', 'topic', 'network', 'skill'
    pattern_data JSON,
    confidence FLOAT,
    learned_at TIMESTAMP,
    seed BIGINT  -- SPI seed for reproducibility
);

-- Temporal pattern query
SELECT
    EXTRACT(HOUR FROM created_at) as hour,
    EXTRACT(DOW FROM created_at) as day_of_week,
    COUNT(*) as post_count,
    AVG(response_time_minutes) as avg_response_time
FROM interactions
GROUP BY hour, day_of_week
ORDER BY post_count DESC;

Python Predictor

# agent_o_rama.py
import jax
import jax.numpy as jnp
from dataclasses import dataclass

@dataclass
class InteractionPredictor:
    learning_rate: float = 0.01
    epochs: int = 100
    batch_size: int = 32
    seed: int = 0xf061ebbc2ca74d78
    
    def fit(self, db_path: str, table: str, validation_split: float = 0.2):
        """Train on DuckDB interaction sequences."""
        import duckdb
        conn = duckdb.connect(db_path)
        
        data = conn.execute(f"SELECT * FROM {table}").fetchall()
        # JAX training loop with SPI seed
        key = jax.random.PRNGKey(self.seed)
        
        for epoch in range(self.epochs):
            key, subkey = jax.random.split(key)
            # ... training logic
            
    def predict(self, history):
        """Predict next interaction given history."""
        return self.model(history)

Ruby Skill Discovery