图 书 馆
贪婪搜索加权规则集的迭代学习Iterative Learning of Weighted Rule Sets for Greedy Search |
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| 课程网址: | http://videolectures.net/icaps2010_xu_greedysearch/ |
| 主讲教师: | Yuehua Xu |
| 开课单位: | 俄勒冈州立大学 |
| 开课时间: | 2010-11-15 |
| 课程语种: | 英语 |
| 中文简介: |  贪婪搜索通常用于尝试以完整性和最优性为代价快速生成解决方案。在这项工作中，我们考虑学习加权操作选择规则的集合，以指导贪婪搜索及其在自动计划中的应用。对于先前的贪婪搜索学习工作，我们做出了两个主要贡献。首先，我们引入加权的行动选择规则集，作为贪婪搜索的一种新形式的控制知识。先前的工作显示了动作选择规则在贪婪搜索中的效用，但已将规则视为硬约束，从而导致了脆弱性。我们的加权规则集允许对多个规则进行投票，从而有助于提高对嘈杂规则的鲁棒性。其次，我们给出了一种新的迭代学习算法，用于基于RankBoost（一种高效的排名提升算法）来学习加权规则集。每次迭代都会考虑当前规则集的实际性能，并根据观察到的搜索错误指导学习。这与大多数现有方法相反，大多数现有方法独立于搜索过程来学习控制知识。我们的经验结果表明，该方法在许多领域都具有广阔的前景。 |
| 课程简介: | Greedy search is commonly used in an attempt to generate solutions quickly at the expense of completeness and optimality. In this work, we consider learning sets of weighted action-selection rules for guiding greedy search with application to automated planning. We make two primary contributions over prior work on learning for greedy search. First, we introduce weighted sets of action-selection rules as a new form of control knowledge for greedy search. Prior work has shown the utility of action-selection rules for greedy search, but has treated the rules as hard constraints, resulting in brittleness. Our weighted rule sets allow multiple rules to vote, helping to improve robustness to noisy rules. Second, we give a new iterative learning algorithm for learning weighted rule sets based on RankBoost, an efficient boosting algorithm for ranking. Each iteration considers the actual performance of the current rule set and directs learning based on the observed search errors. This is in contrast to most prior approaches, which learn control knowledge independently of the search process. Our empirical results have shown significant promise for this approach in a number of domains. |
| 关 键 词: | 贪婪搜索; 迭代学习; 算法搜索 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-03-07：zyk |
| 最后编审: | 2021-03-10：zyk |
| 阅读次数: | 62 |