# Algebraic Auction Studio for Robotic Fleet (AAS-RF)

This repository implements a production-grade MVP of the Algebraic Auction Studio for robotic fleets.

- DSL to declare agents (robots), tasks, budgets, and preferences
- Compositional optimization engine (ADMM-lite) for distributed fleet allocation
- Offline-first runtime with delta-sync and tamper-evident audit logs
- CatOpt-inspired data contracts and adapters registry for heterogeneous platforms
- Governance, privacy budgeting, and an adapters marketplace scaffold
- Phase-driven MVP plan with HIL testing capabilities

This package is structured as a Python project under the package name `algebraic_auction_studio_for_robotic_fle`.
See the AGENTS.md for architecture details and testing commands.

Usage: see tests under `tests/` for examples and run `bash test.sh` to verify CI-like flows locally.

Engine: ADMM-lite Fleet Allocation
- A minimal, production-facing Python module `engine_admm.py` exposes admm_solve(agents, tasks) to compute a fleet-wide allocation.
- It demonstrates a compositional optimization approach where local agent costs influence task assignment.
- The tests under `tests/` validate basic behavior and can be extended for more elaborate scenarios.

Licensing: MIT (placeholder; update as needed)

"""
Note: This README is intentionally concise to keep the repo developer-focused. A more detailed marketing/tech readme should accompany a real release.
"""