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Backtracking Search Algorithms
, 2006
"... There are three main algorithmic techniques for solving constraint satisfaction problems: backtracking search, local search, and dynamic programming. In this chapter, I survey backtracking search algorithms. Algorithms based on dynamic programming [15]— sometimes referred to in the literature as var ..."
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Cited by 19 (2 self)
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There are three main algorithmic techniques for solving constraint satisfaction problems: backtracking search, local search, and dynamic programming. In this chapter, I survey backtracking search algorithms. Algorithms based on dynamic programming [15]— sometimes referred to in the literature as variable elimination, synthesis, or inference algorithms—are the topic of Chapter 7. Local or stochastic search algorithms are the topic of Chapter 5. An algorithm for solving a constraint satisfaction problem (CSP) can be either complete or incomplete. Complete, or systematic algorithms, come with a guarantee that a solution will be found if one exists, and can be used to show that a CSP does not have a solution and to find a provably optimal solution. Backtracking search algorithms and dynamic programming algorithms are, in general, examples of complete algorithms. Incomplete, or nonsystematic algorithms, cannot be used to show a CSP does not have a solution or to find a provably optimal solution. However, such algorithms are often effective at finding a solution if one exists and can be used to find an approximation to an optimal solution. Local or stochastic search algorithms are examples of incomplete algorithms. Of the two
Determining the basis for performance variation in CSP heuristics £
"... This paper develops the idea that variable ordering heuristics can be classified on the basis of a small number of distinguishable actions, and that while specific heuristics may be classified differently depending on the problem type, the basic actions that determine their classification are the sa ..."
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This paper develops the idea that variable ordering heuristics can be classified on the basis of a small number of distinguishable actions, and that while specific heuristics may be classified differently depending on the problem type, the basic actions that determine their classification are the same. Previous work demonstrated two basic categories of heuristics, and that problems in an apparently homogeneous problem set differ in their amenability to heuristics of different types. The present paper shows that these heuristic actions, which may be described as building up contention and propagating effects, have distinct values for descriptive measures such as depth of failure and the depth at which a problem becomes tractable, that reflect differences in the rapidity of their effects with respect to search depth. Heuristics behave similarly with respect to their basic actions across a wide range of propagation, from simple backtracking to maintained arc consistency. The propagationofeffects type of action is closely related to the “simplification hypothesis ” of Hooker and Vinay. This work contributes to the goals of explaining heuristic performance and putting heuristic selection on a rational basis. 1.