图 书 馆
通过扩展贝叶斯逻辑程序识别诱导计划Abductive Plan Recognition by Extending Bayesian Logic Programs |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_raghavan_abductive/ |
| 主讲教师: | Sindhu Raghavan |
| 开课单位: | 德克萨斯大学 |
| 开课时间: | 2011-11-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Plan recognition is the task of predicting an agent’s top-level plans based on its observed actions. It is an abductive reasoning task that involves inferring cause from effect. Most existing approaches to plan recognition use either first-order logic or probabilistic graphical models. While the former cannot handle uncertainty, the latter cannot handle structured representations. In order to overcome these limitations, we develop an approach to plan recognition using Bayesian Logic Programs (BLPs), which combine first-order logic and Bayesian networks. Since BLPs employ logical deduction to construct the networks, they cannot be used effectively for plan recognition. Therefore, we extend BLPs to use logical abduction to construct Bayesian networks and call the resulting model Bayesian Abductive Logic Programs (BALPs). We learn the parameters in BALPs using the Expectation Maximization algorithm adapted for BLPs. Finally, we present an experimental evaluation of BALPs on three benchmark data sets and compare its performance with the state-of-the-art for plan recognition. |
| 关 键 词: | 计划识别; 贝叶斯逻辑程序; 一阶逻辑 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-10：yumf |
| 阅读次数: | 49 |