图 书 馆
贪婪搜索的加权规则集迭代学习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: | 2010-05-10 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 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: | 计算机科学; 机器学习; 贪婪搜索 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-11-17：cwx |
| 阅读次数: | 30 |