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Intelligent search strategies based on adaptive Constraint Handling Rules

机译:基于自适应约束处理规则的智能搜索策略

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The most advanced implementation of adaptive constraint processing with Constraint Handling Rules (CHR) allows the application of intelligent search strategies to solve Constraint Satisfaction Problems (CSP). This presentation compares an improved version of conflict-directed backjumping and two variants of dynamic backtracking with respect to chronological backtracking on some of the AIM instances which are a benchmark set of random 3-SAT problems. A CHR implementation of a Boolean constraint solver combined with these different search strategies in Java is thus being compared with a CHR implementation of the same Boolean constraint solver combined with chronological backtracking in SICStus Prolog. This comparison shows that the addition of "intelligence" to the search process may reduce the number of search steps dramatically. Furthermore, the runtime of their Java implementations is in most cases faster than the implementations of chronological backtracking. More specifically, conflict-directed backjumping is even faster than the SICStus Prolog implementation of chronological backtracking, although our Java implementation of CHR lacks the optimisations made in the SICStus Prolog system.
机译:带有约束处理规则(CHR)的自适应约束处理的最先进实现方式允许应用智能搜索策略来解决约束满足问题(CSP)。此演示文稿比较了针对冲突的回跳的改进版本和动态回溯的两种变体,它们涉及一些AIM实例上的按时间顺序回溯,这些AIM实例是随机3-SAT问题的基准集。因此,正在将布尔约束求解器的CHR实现与Java中的这些不同搜索策略组合在一起,并与SICStus Prolog中的相同布尔约束求解器的CHR实现与按时间顺序回溯相结合进行比较。这种比较表明,在搜索过程中添加“情报”可以显着减少搜索步骤的数量。此外,在大多数情况下,其Java实现的运行时间比按时间顺序回溯的实现快。更具体地说,尽管我们的CHR的Java实施缺少SICStus Prolog系统中的优化,但冲突导向的回跳甚至比SICStus Prolog按时间顺序回溯的实现还要快。

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