Can I use this for topic modeling?

Yes, it includes specialized capabilities to extract topic distributions, Markov chain transitions, and hierarchical clusters from text-based interaction data.

What is a cognitive surrogate in Agent-o-rama?

A cognitive surrogate is an AI system designed to mimic or predict the interaction patterns and expertise of a specific entity or persona based on historical interaction data.

How does Agent-o-rama differ from traditional ML training?

It supports both traditional stochastic epoch-based training and 'Unworld' derivational generation, which is 100x faster, lower cost, and fully deterministic.

What database does this skill integrate with?

Agent-o-rama is natively integrated with DuckDB for managing interaction sequences, training data, and storing learned behavioral patterns.

What is the purpose of the bisimulation game feature?

The bisimulation game is a formal verification tool used to prove that patterns generated via different methods (like temporal training vs. unworld derivation) are behaviorally equivalent.

Agent-o-rama Cognitive Learning

Name: Agent-o-rama Cognitive Learning
Author: plurigrid

byplurigrid

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Ciencia de Datos y ML

Extracts behavioral patterns and trains learning agents for cognitive surrogate systems using temporal and topic analysis.

Acerca de

Agent-o-rama is a sophisticated cognitive learning layer that analyzes interaction sequences to discover latent behavioral patterns. It provides tools for training interaction predictors, extracting temporal and topic-based dynamics, and identifying specific skills from raw data. By integrating both traditional epoch-based training and deterministic 'Unworld' derivational generation, it allows developers to build high-fidelity cognitive surrogates that mimic specific interaction styles and expertise within a DuckDB-backed infrastructure.

Características Principales

Extract temporal patterns including peak activity hours, engagement cycles, and response times
2 GitHub stars
Analyze topic dynamics, transitions, and entropy using advanced embedding models
Automate skill discovery to identify latent expertise and behavioral coherence in interaction logs
Train interaction predictors using epoch-based learning or deterministic derivational chaining
Verify behavioral equivalence between learned and derived models via bisimulation games

Casos de Uso

Analyzing large-scale interaction datasets to optimize community engagement and response patterns
Building digital twins or cognitive surrogates for specific users or subject matter experts
Deterministically generating behavioral models for predictable AI agent performance

Acerca de

Características Principales

Extract temporal patterns including peak activity hours, engagement cycles, and response times
2 GitHub stars
Analyze topic dynamics, transitions, and entropy using advanced embedding models
Automate skill discovery to identify latent expertise and behavioral coherence in interaction logs
Train interaction predictors using epoch-based learning or deterministic derivational chaining
Verify behavioral equivalence between learned and derived models via bisimulation games

Casos de Uso

Analyzing large-scale interaction datasets to optimize community engagement and response patterns
Building digital twins or cognitive surrogates for specific users or subject matter experts
Deterministically generating behavioral models for predictable AI agent performance