PROMETHEUS — An Intrinsically Motivated Cognitive Architecture for Autonomous Scientific Knowledge Discovery
PROMETHEUS is an AI research project focused on cognitive architectures, knowledge representation, scientific knowledge discovery, intrinsic motivation, curiosity-driven learning, knowledge graphs, and autonomous reasoning.
Phase: Phase 1 — Execution Platform Preparation
The project specifications and engineering documentation have been established and validated. Implementation is now progressing through the approved implementation roadmap.
- Repository foundation and canonical project structure
- Engineering and product documentation baseline
- Development tooling baseline
- CI quality checks
- Pre-commit validation
- Development Docker container baseline
- Container build and runtime validation
- Ruff, Black, and Pytest validation inside the development container
- Python package dependency validation with
pip check
Phase 1 — Execution Platform Preparation
The current implementation focus is establishing the execution and infrastructure foundation before higher-level platform services and cognitive modules are implemented.
- Scope & Research Boundary
- Design Decision Log
- Module Specification
- System Architecture
- Implementation Roadmap
- Project File Structure
- Development Workflow
- Claude Implementation Guide
The project currently provides a development Docker image based on Python 3.13.
The development container includes the project's development tooling and runs under a dedicated non-root user.
Container validation currently includes:
- Ruff
- Black
- Pytest
pip check
PROMETHEUS follows an architecture-first, verification-driven development process.
Implementation must remain consistent with the approved project specifications.