Uncertainty in Artificial Intelligence
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Sequential Bayesian Optimisation for Spatial-Temporal Monitoring
Roman Marchant, Fabio Ramos, Scott Sanner
Abstract:
Bayesian Optimisation has received considerable attention in recent years as a general methodol- ogy to find the maximum of costly-to-evaluate objective functions. Most existing BO work fo- cuses on where to gather a set of samples with- out giving special consideration to the sampling sequence, or the costs or constraints associated with that sequence. However, in real-world sequential decision problems such as robotics, the order in which samples are gathered is paramount, especially when the robot needs to optimise a temporally non-stationary objective function. Additionally, the state of the environ- ment and sensing platform determine the type and cost of samples that can be gathered. To address these issues, we formulate Sequential Bayesian Optimisation (SBO) with side-state in- formation within a Partially Observed Markov Decision Process (POMDP) framework that can accommodate discrete and continuous observa- tion spaces. We build on previous work using Monte-Carlo Tree Search (MCTS) and Upper Confidence bound for Trees (UCT) for POMDPs and extend it to work with continuous state and observation spaces. Through a series of experi- ments on monitoring a spatial-temporal process with a mobile robot, we show that our UCT- based SBO POMDP optimisation outperforms myopic and non-myopic alternatives.
Keywords:
Pages: 553-562
PS Link:
PDF Link: /papers/14/p553-marchant.pdf
BibTex:
@INPROCEEDINGS{Marchant14,
AUTHOR = "Roman Marchant and Fabio Ramos and Scott Sanner",
TITLE = "Sequential Bayesian Optimisation for Spatial-Temporal Monitoring",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "553--562"
}


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