Planning under Uncertainty with Weighted State Scenarios
Conference Paper
(2015)
URL related publication
http://www.aaai.org/ocs/index.php/FSS/FSS15/paper/view/11689/11514
Final published version
To reference this document use
https://resolver.tudelft.nl/uuid:8499d63a-c7fc-4214-9f18-30ababfaa62a
More Info
expand_more
expand_more
Publication Year
2015
Language
English
Pages (from-to)
93-94
Event
Downloads counter
127
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.