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.
Original languageEnglish
Title of host publicationProceedings of the 31st Conference on Uncertainty in Artificial Intelligence
Number of pages10
StatePublished - 13 Jul 2015
Event31th Conference on Uncertainty in Artificial Intelligence - Amsterdam, Netherlands
Duration: 12 Jul 201517 Jul 2015


Conference31th Conference on Uncertainty in Artificial Intelligence
Abbreviated titleUAI
Internet address

ID: 10648043