解决计算难题的贝叶斯方法
A Bayesian Approach to Tackling Hard Computational Problems
摘要：
我们正在开发一个通用的框架，使用学习贝叶斯模型的决策理论控制的搜索和reasoningalgorithms。我们说明的具体任务，控制一般和特定领域的求解器上的硬类结构约束满足问题的方法。一个成功的策略，以减少高（甚至无限）的变化，在运行时间通常表现出回溯搜索算法是切断和重新启动搜索，如果一个解决方案没有找到在一定的时间。以前的工作重新启动策略采用固定的截止值。我们展示了如何通过学习贝叶斯模型来创建动态截止策略，该模型基于观察搜索算法的早期行为来预测试验的最终长度。此外，我们描述的一般条件下，动态重启策略可以优于理论上最优的固定策略。
英文摘要：
We are developing a general framework for using learned Bayesian models for decision-theoretic control of search and reasoningalgorithms. We illustrate the approach on the specific task of controlling both general and domain-specific solvers on a hard class of structured constraint satisfaction problems. A successful strategyfor reducing the high (and even infinite) variance in running time typically exhibited by backtracking search algorithms is to cut off and restart the search if a solution is not found within a certainamount of time. Previous work on restart strategies have employed fixed cut off values. We show how to create a dynamic cut off strategy by learning a Bayesian model that predicts the ultimate length of a trial based on observing the early behavior of the search algorithm. Furthermore, we describe the general conditions under which a dynamic restart strategy can outperform the theoretically optimal fixed strategy.
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