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. """