MAAT-Core is a general-purpose framework for ethical constrained optimization.
It can be applied to any domain where decisions must be optimized within hard boundaries.
Instead of optimizing freely and checking ethics afterwards, MAAT-Core embeds ethical principles directly into the mathematical structure of the system.
In MAAT-Core:
- Fields describe what the system wants.
- Constraints describe what the system must never violate.
If a constraint is violated, the solution becomes mathematically unstable.
Use cases:
- Autonomous agents with forbidden states
- Safe language models
- Hard alignment boundaries
Why MAAT-Core fits:
- Safety is a hard constraint.
- Unsafe solutions do not exist in the solution space.
- No post-filtering or heuristics required.
This enables Safety by Construction.
Use cases:
- Drones with no-fly zones
- Self-driving cars with safety distances
- Industrial robots with physical limits
Model:
- Fields: efficiency, speed, energy
- Constraints: safety, collision avoidance, stability
Use cases:
- Logistics
- Production planning
- Traffic optimization
- Smart grids
MAAT-Core acts as a general constrained optimizer with explainability.
Use cases:
- Energy distribution with CO₂ limits
- Water management
- Fair resource sharing
Use cases:
- Credit scoring
- Hiring algorithms
- Recommendation systems
Ethics becomes a mathematical boundary, not a post-check.
Every solution can be analyzed using:
core.constraint_report(state)Trade-offs between safety and efficiency become explicit.
Useful for modeling allowed state spaces.
Coordination without explicit rules.
Ideal for teaching AI ethics and optimization.
MAAT-Core can be applied wherever a system must optimize something, but must never violate certain principles.
In short:
MAAT-Core is a universal ethics compiler for decision systems.