Data-driven Generation of Policies

Data-driven Generation of Policies

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...

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Bibliographic Details
Published in:Springer eBooks
Main Authors: Parker, Austin (Author), Simari, Gerardo I. (Author), Sliva, Amy (Author), Subrahmanian, V.S (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: New York, NY : Springer New York : Imprint: Springer, 2014.
Series:SpringerBriefs in Computer Science,
Subjects:
Computer Science.
Database management.
Data mining.
Artificial intelligence.
Artificial Intelligence (incl. Robotics).
Data Mining and Knowledge Discovery.
Database Management.
Probability and Statistics in Computer Science.
Online Access:http://dx.doi.org/10.1007/978-1-4939-0274-3
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Table of Contents:
  • Introduction and Related Work
  • Optimal State Change Attempts
  • Different Kinds of Effect Estimators
  • A Comparison with Planning under Uncertainty
  • Experimental Evaluation
  • Conclusions.

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