Data-driven Generation of Policies

  • Austin Parker
  • Gerardo I. Simari
  • Amy Sliva
  • V.S. Subrahmanian

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-x
  2. Austin Parker, Gerardo I. Simari, Amy Sliva, V. S. Subrahmanian
    Pages 1-7
  3. Austin Parker, Gerardo I. Simari, Amy Sliva, V. S. Subrahmanian
    Pages 9-18
  4. Austin Parker, Gerardo I. Simari, Amy Sliva, V. S. Subrahmanian
    Pages 19-29
  5. Austin Parker, Gerardo I. Simari, Amy Sliva, V. S. Subrahmanian
    Pages 31-35
  6. Austin Parker, Gerardo I. Simari, Amy Sliva, V. S. Subrahmanian
    Pages 37-45
  7. Austin Parker, Gerardo I. Simari, Amy Sliva, V. S. Subrahmanian
    Pages 47-48
  8. Back Matter
    Pages 49-50

About this book

Introduction

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.

Keywords

Automatic policy generation Data-driven information systems Effect estimators Event databases Trie data structure

Authors and affiliations

  • Austin Parker
    • 1
  • Gerardo I. Simari
    • 2
  • Amy Sliva
    • 3
  • V.S. Subrahmanian
    • 4
  1. 1.Department of Computer ScienceUniversity of MarylandCollege ParkUSA
  2. 2.Department of Computer ScienceUniversity of OxfordOxfordUnited Kingdom
  3. 3.Charles River Analytics Inc.CambridgeUSA
  4. 4.Computer Science DepartmentUniversity of MarylandCollege ParkUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4939-0274-3
  • Copyright Information The Author(s) 2014
  • Publisher Name Springer, New York, NY
  • eBook Packages Computer Science
  • Print ISBN 978-1-4939-0273-6
  • Online ISBN 978-1-4939-0274-3
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • About this book
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