Planning under Uncertainty with Weighted State Scenarios

Planning under Uncertainty with Weighted State Scenarios

Walraven, E.M.P.
Spaan, M.T.J.

Electrical Engineering, Mathematics and Computer Science

Software Technology

2015-07-13

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.

planning under uncertainty
smart grids
renewable energy

http://resolver.tudelft.nl/uuid:1793be16-0da0-41ce-83df-f5c30cccda8a

AUAI Press

Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI), Amsterdam,The Netherlands, 2-16 July 2015

Institutional Repository

conference paper

(c) 2015 Walraven, E.M.P.