Planning under Uncertainty with Weighted State Scenarios

Conference paper (2015)

Authors

E.M.P. Walraven Algorithmics - EEMCS
M.T.J. Spaan Algorithmics - EEMCS
Research Group
Algorithmics (EEMCS) (TU Delft)
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Published Date

13-07-2015

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Algorithmics

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.