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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.
Original language | English |
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Title of host publication | Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence |
Pages | 912-921 |
Number of pages | 10 |
Publication status | Published - 13 Jul 2015 |
Event | 31th Conference on Uncertainty in Artificial Intelligence - Amsterdam, Netherlands Duration: 12 Jul 2015 → 17 Jul 2015 http://auai.org/uai2015/index.html |
Conference
Conference | 31th Conference on Uncertainty in Artificial Intelligence |
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Abbreviated title | UAI |
Country/Territory | Netherlands |
City | Amsterdam |
Period | 12/07/15 → 17/07/15 |
Internet address |
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Dive into the research topics of 'Planning under Uncertainty with Weighted State Scenarios'. Together they form a unique fingerprint.Projects
- 1 Finished
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GCP: Gaming beyond the Copper Plate
de Weerdt, M. M., Spaan, M. T. J., de Vries, L. J., Witteveen, C., van der Sluis, L., Walraven, E. M. P., Philipsen, R. M., Morales Espana, G. A. & Ramirez Elizondo, L. M.
1/10/14 → 31/10/18
Project: Research