Abstract

. We present a detailed experimental comparison of interleaved depth-first search and depth-bounded discrepancy search, two tree search procedures recently developed with the same goal: to reduce the cost of heuristic mistakes at the top of the tree. Our comparison uses an abstract heuristic model, and three different concrete problem classes: binary constraint satisfaction, quasigroup completion and number partitioning problems. Results indicate that both search strategies often reduce search. In addition, they show that their efficiency depends on a trade-off between the number of discrepancies (branch points against the heuristic) considered at the top of the tree, and the overhead of expanding branches from these discrepancies. If the number of discrepancies is large, the overhead can outweigh the benefits. 1 INTRODUCTION By definition, heuristics sometimes make mistakes. When searching a tree with depth-first search (Dfs), mistakes made at the top of the tree can be very costly ...

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