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dc.contributor.author
Parker, Austin
dc.contributor.author
Simari, Gerardo
dc.contributor.author
Sliva, Amy
dc.contributor.author
Subrahmanian, Venkatramanan
dc.date.available
2021-07-02T15:13:39Z
dc.date.issued
2014
dc.identifier.citation
Parker, Austin; Simari, Gerardo; Sliva, Amy; Subrahmanian, Venkatramanan; Data-driven Generation of Policies; Springer; 2014; 60
dc.identifier.isbn
978-1-4939-0273-6
dc.identifier.issn
2191-5768
dc.identifier.uri
http://hdl.handle.net/11336/135399
dc.description.abstract
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 advanced-level students of computer science.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer
dc.rights
info:eu-repo/semantics/closedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.source
https://link.springer.com/bookseries/10028
dc.subject
AUTOMATIC POLICY GENERATION
dc.subject
DATA-DRIVEN INFORMATION SYSTEMS
dc.subject
EFFECT ESTIMATORS
dc.subject
EVENT DATABASES
dc.subject
TRIE DATA STRUCTURE
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Data-driven Generation of Policies
dc.type
info:eu-repo/semantics/publishedVersion
dc.type
info:eu-repo/semantics/book
dc.type
info:ar-repo/semantics/libro
dc.date.updated
2021-06-07T15:37:03Z
dc.identifier.eissn
2191-5776
dc.journal.pagination
60
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Nueva York
dc.description.fil
Fil: Parker, Austin. University of Maryland; Estados Unidos
dc.description.fil
Fil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. University of Oxford; Reino Unido
dc.description.fil
Fil: Sliva, Amy. Charles River Analytics Inc.; Estados Unidos
dc.description.fil
Fil: Subrahmanian, Venkatramanan. University of Maryland; Estados Unidos
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.springer.com/computer/ai/book/978-1-4939-0273-6
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1007/978-1-4939-0274-3
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