Preference-Based Search for Scheduling (original) (raw)
2000, National Conference on Artificial Intelligence
Preference-based search (PBS) is a new search procedure for solving combinatorial optimization problems. Given a set of preferences between search decisions, PBS searches through a space of preferred solutions, which is tighter than the space of all solutions. The definition of preferred solutions is based on work in non-monotonic reasoning (Brewka 1989; Geffner & Pearl 1992; Grosof 1991) on priorities between defaults. The basic idea of PBS is quite simple: Always pick a locally best decision α. Either make the decision α or make other locally best decisions that allow to deduce ¬α and thus represent a counterargument for α. If there is no possible counterargument then PBS does not explore the subtree of ¬α. This pruning of the search space is obtained by nonmonotonic inference rules that are inspired by Doyle's TMS and that detect decisions belonging to all or no preferred solution. We show that PBS can optimally solve various important scheduling problems.
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