| 书目名称 | Data-driven Generation of Policies |
| 编辑 | Austin Parker,Gerardo I. Simari,V.S. Subrahmanian |
| 视频video | |
| 概述 | Includes supplementary material: |
| 丛书名称 | SpringerBriefs in Computer Science |
| 图书封面 |  |
| 描述 | This Springer Brief presents a basic algorithm that provides a correct solution to finding an optimal state change attempt, as well as an enhanced algorithm that is built on top of the well-known trie data structure. It explores correctness and algorithmic complexity results for both algorithms and experiments comparing their performance on both real-world and synthetic data. Topics addressed include optimal state change attempts, state change effectiveness, different kind of effect estimators, planning under uncertainty and experimental evaluation. These topics will help researchers analyze tabular data, even if the data contains states (of the world) and events (taken by an agent) whose effects are not well understood. Event DBs are omnipresent in the social sciences and may include diverse scenarios from political events and the state of a country to education-related actions and their effects on a school system. With a wide range of applications in computer science and the social sciences, the information in this Springer Brief is valuable for professionals and researchers dealing with tabular data, artificial intelligence and data mining. The applications are also useful for a |
| 出版日期 | Book 2014 |
| 关键词 | Automatic policy generation; Data-driven information systems; Effect estimators; Event databases; Trie d |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4939-0274-3 |
| isbn_softcover | 978-1-4939-0273-6 |
| isbn_ebook | 978-1-4939-0274-3Series ISSN 2191-5768 Series E-ISSN 2191-5776 |
| issn_series | 2191-5768 |
| copyright | The Author(s) 2014 |
发表于 2025-3-21 19:21:16
