Publications

Planning under Uncertainty with Weighted State Scenarios

Erwin Walraven and Matthijs T. J. Spaan. Planning under Uncertainty with Weighted State Scenarios. In Proc. of Uncertainty in Artificial Intelligence, pp. 912–921, 2015.

Download

pdf [250.1kB]  

Abstract

In many planning domains external factors are hard to model using a compact Markovian state. However, long-term dependencies between consecutive states of an environment might exist, which can be exploited during planning. In this paper we propose a scenario representation which enables agents to reason about sequences of future states. We show how weights can be assigned to scenarios, representing the likelihood that scenarios predict future states. Furthermore, we present a model based on a Partially Observable Markov Decision Process (POMDP) to reason about state scenarios during planning. In experiments we show how scenarios and our POMDP model can be used in the context of smart grids and stock markets, and we show that our approach outperforms other methods for decision making in these domains.

BibTeX Entry

@InProceedings{Walraven15uai,
  author =       {Erwin Walraven and Matthijs T. J. Spaan},
  title =        {Planning under Uncertainty with Weighted State Scenarios},
  booktitle =    {Proc. of Uncertainty in Artificial Intelligence},
  year =         2015,
  pages =        {912--921}
}

Note: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Generated by bib2html.pl (written by Patrick Riley) on Wed Apr 10, 2019 18:58:24 UTC