27 lines
1.4 KiB
Markdown
27 lines
1.4 KiB
Markdown
# Algebraic Auction Studio for Robotic Fleet (AAS-RF)
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This repository implements a production-grade MVP of the Algebraic Auction Studio for robotic fleets.
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- DSL to declare agents (robots), tasks, budgets, and preferences
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- Compositional optimization engine (ADMM-lite) for distributed fleet allocation
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- Offline-first runtime with delta-sync and tamper-evident audit logs
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- CatOpt-inspired data contracts and adapters registry for heterogeneous platforms
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- Governance, privacy budgeting, and an adapters marketplace scaffold
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- Phase-driven MVP plan with HIL testing capabilities
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This package is structured as a Python project under the package name `algebraic_auction_studio_for_robotic_fle`.
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See the AGENTS.md for architecture details and testing commands.
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Usage: see tests under `tests/` for examples and run `bash test.sh` to verify CI-like flows locally.
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Engine: ADMM-lite Fleet Allocation
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- A minimal, production-facing Python module `engine_admm.py` exposes admm_solve(agents, tasks) to compute a fleet-wide allocation.
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- It demonstrates a compositional optimization approach where local agent costs influence task assignment.
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- The tests under `tests/` validate basic behavior and can be extended for more elaborate scenarios.
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Licensing: MIT (placeholder; update as needed)
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"""
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Note: This README is intentionally concise to keep the repo developer-focused. A more detailed marketing/tech readme should accompany a real release.
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"""
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