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)
Renewable energy Smart grids Planning under uncertainty Markov decision processes
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Published Date

2015

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Algorithmics

Abstract

External factors are hard to model using a Markovian state in several real-world planning domains. Although planning can be difficult in such domains, it may be possible to exploit long-term dependencies between states of the environment during planning. We introduce weighted state scenarios to model long-term sequences of states, and we use a model based on a Partially Observable Markov Decision Process to reason about scenarios during planning. Experiments show that our model outperforms other methods for decision making in two real-world domains.