# 🧠 Ontology Architect

### Autonomous Scientific Discovery in Non-Stationary Worlds

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## 🚀 Overview

**Ontology Architect** is an experimental AI system designed to move beyond traditional prediction-based machine learning toward **autonomous scientific discovery**.

Instead of simply learning patterns from data, our agent:

* Observes noisy, unstructured sensor logs
* Infers hidden variables
* Writes its own Python-based theories
* Continuously refines those theories as new data arrives

This project is inspired by how real scientists work:
**hypothesize → test → falsify → revise**

---

## ❗ The Problem

Most modern AI systems are **model-free**:

* They learn correlations
* They require large labeled datasets
* They assume the world is **stationary**

However, real-world systems are very different:

* Observations are noisy
* True variables are hidden
* System dynamics change over time

### Example

In industrial systems, sensors measure vibration and temperature—but not internal stress or wear.
Traditional models detect anomalies but **cannot explain or adapt to them**.

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## 💡 Our Idea

We propose an AI agent that behaves like a **scientist** rather than a predictor.

The agent:

1. **Observes** raw logs
2. **Builds a theory** (Python code with latent variables and equations)
3. **Tests predictions** against future data
4. **Refines or replaces** the theory

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## ⚙️ Environment Design

### Observation

* Raw text logs of sensor values
* Output from previous theory execution
* Peer-review logs of failed hypotheses

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### Action

* A **full rewrite or diff** of a Python theory module
* Limited to ~2k tokens

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### Reward Function

We follow the **Minimum Description Length (MDL)** principle:

```
Reward = log P(observations | theory) - λ * complexity(theory)
```

Where:

* First term → predictive accuracy
* Second term → penalizes overly complex theories

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### Twist: Non-Stationarity

Every few episodes:

* The underlying “laws of physics” change

The agent must:

* Detect performance degradation
* Trigger a **paradigm shift**
* Replace its theory instead of patching it

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## 🧩 Theory Representation

To keep the search space manageable, theories follow a structured format:

```python
class Theory:
    def latent_variables(self, obs):
        return {...}

    def dynamics(self, state):
        return {...}

    def observe(self, state):
        return {...}
```

This ensures:

* Interpretability
* Safe execution
* Structured exploration

---

## 🔄 Learning Loop

```
Observe → Evaluate → Analyze Residuals → Propose Update → Execute → Score → Repeat
```

The agent balances:

* **Exploitation** → refining current theory
* **Exploration** → proposing new structures

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## 🧠 What Makes This Novel

Unlike traditional ML systems, this approach:

* Invents **latent variables**
* Searches over **theory space**, not just parameters
* Uses **falsification** as a learning signal
* Handles **changing environments** via paradigm shifts

This is closer to **scientific reasoning** than standard AI training.

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## 🌍 Why This Matters

This approach is useful in domains where:

* The system is **partially observable**
* The true structure is unknown
* The environment **changes over time**

### Potential Applications

* 🏭 Industrial monitoring
* 🧬 Biology & drug discovery
* 🌍 Climate modeling
* 📈 Financial systems
* 🧠 Neuroscience

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## 🔗 Project Links

* GitHub Repository:
  [https://github.com/rohan-27p/Ontology-architect](https://github.com/rohan-27p/Ontology-architect)

* Hugging Face Space:
  [https://huggingface.co/spaces/LostDecimal/alien-physics-openenv/tree/main](https://huggingface.co/spaces/LostDecimal/alien-physics-openenv/tree/main)

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## 📊 Training Results

## Overview

![Training Overview](artifacts/results/training_overview.png)

## Discovery

![Discovery Plot](artifacts/results/discovery_plot.png)

### 1. GRPO Traning Curve

![Grpo Training Curve](artifacts/results/grpo_training_curve.png)

### 2. SFT Loss Curve

![SFT Training Curve](artifacts/results/sft_training_loss.png)

### 3. Reward Curve

![Reward Curve](artifacts/results/reward_curve.png)

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## 🧪 Demo Scenario

In our simulation:

* Phase 1 → Linear dynamics
* Phase 2 → Oscillatory dynamics

The agent:

* Initially fails after the shift
* Detects mismatch
* Introduces new latent structure
* Recovers predictive accuracy

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## 🎯 Conclusion

We are moving from:

> AI that **predicts data**
> to
> AI that **explains the world**

Ontology Architect is a step toward systems that:

* Discover structure
* Adapt to change
* Build and revise knowledge autonomously

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## 🙌 Acknowledgment

Built as part of a hackathon exploring the future of AI-driven scientific discovery.