Constraint Learning

TL;DR for operators Most decision systems treat hard constraints like a trapdoor. Candidate violates requirement, candidate disappears. Efficient, clean, and occasionally absurd. The paper behind Repair-Augmented Constraint Learning, or RACL, argues that this is the wrong semantics for systems that already know how to modify an option before showing it to the user.1 A flight missing a checked bag, a hotel missing breakfast, a product bundle missing an accessory, or a schedule slot needing a resource adjustment may not be a bad option. It may be a good option one repair away from being acceptable. ...

The spreadsheet rule that never quite reaches the model Rules are everywhere in business software. An invoice total must match its line items. A loan file must contain the right documents before underwriting. A production schedule cannot assign the same machine to two jobs at the same time. A compliance workflow may tolerate uncertainty in OCR, but not uncertainty about whether a prohibited combination of fields has appeared. ...

A shift schedule can be perfectly valid and still be a terrible policy manual. Consider a care-facility manager facing an unpleasant Wednesday: several employees have requested leave, available staffing barely covers demand, and somebody must work a day shift immediately after completing a night shift. The manager makes the assignment because residents still require care. The completed roster records what happened. It does not necessarily record what the facility considers acceptable under normal conditions. ...