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Understand the concept of weak commitment in constraint processing, where constraint propagation and search techniques are used to reduce problem size and make decisions while keeping multiple solutions. Discover how least commitment approaches can be applied to various domains such as planning and scheduling.
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Note on Least Commitment Foundations of Constraint Processing CSCE421/821, Spring 2008: www.cse.unl.edu/~choueiry/S08-421-821/ Berthe Y. Choueiry (Shu-we-ri) Avery Hall, Room 123B choueiry@cse.unl.edu Tel: +1(402)472-5444 Weak Commitment
Background • Constraint propagation • Reduces problem size • Eliminates inconsistent choices • Gets the problem closer to being solved • But does not eliminate any solutions • When it solves the problem, we keep all solutions • Search • Solves the problem by making decisions and eliminating perfectly acceptable choices, while keeping one Weak Commitment
Generalizing.. • Constraint propagation makes no commitment at all • Search makes strong commitments • Question: anything in the middle? • Answer: least commitment • Common: planning, scheduling communities Weak Commitment
Least commitment: rationale • As long as you do not need to make a commitment, keep propagating • If propagation is ‘stuck’ (no progress), • then make the least commitment you need to make in order to enable more propagation without ruling out too many solutions • Technique: • typically by adding a constraint (that is weaker than a variable assignment) Weak Commitment
Example: scheduling • Context: • scheduling tasks in time. You notice the 2 tasks must use the same resource • Propagation • adds a mutex constraint between both tasks without ordering them • Search • fixes the time for one or both tasks thus ruling out so many possibilities • Least commitment • adds a constraint committing one task to be before the other • tasks remain floating in time, • and any task can be inserted between them Weak Commitment