Many combinatorial optimization and constraint satisfaction problems can be formulated as a search for the best leaf in a tree of bounded depth. When exhaustive enumeration is infeasible, a rational strategy visits leaves in increasing order of predicted cost. Previous systematic algorithms for this setting follow a predetermined search order, making strong implicit assumptions about predicted cost and using problem-specific information inefficiently. We introduce a framework, best-leaf-first search (BLFS), that employs an explicit model of leaf cost. BLFS is complete and visits leaves in an order that efficiently approximates increasing predicted cost. Different algorithms can be derived by incorporating different sources of information into the cost model. We show how previous algorithms are special cases of BLFS
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