Abstract
In this paper, we propose an extension of three incomplete depth-first search techniques, namely depth-bounded backtrack search, credit search, and iterative broadening, towards producing incomplete solutions. We also propose a new cutoff strategy for incomplete depth-first search motivated by a human style of problem solving. This technique, called limited assignment number (LAN) search, is based on limiting the number of attempts tried to assign a value to the variable. A linear worst-case time complexity of LAN Search leads to promising stable time behavior in all accomplished experiments. The techniques are studied in the context of constraint satisfaction problems.
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