Sequential Bayesian Optimisation for Spatial-Temporal Monitoring

Roman Marchant, Fabio Ramos, Scott Sanner
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:7-16, 2014.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-marchant14a, title = {Sequential {B}ayesian Optimisation for Spatial-Temporal Monitoring}, author = {Marchant, Roman and Ramos, Fabio and Sanner, Scott}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {7--16}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/marchant14a/marchant14a.pdf}, url = {https://proceedings.mlr.press/r12/marchant14a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Sequential Bayesian Optimisation for Spatial-Temporal Monitoring %A Roman Marchant %A Fabio Ramos %A Scott Sanner %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-marchant14a %I PMLR %P 7--16 %U https://proceedings.mlr.press/r12/marchant14a.html %V R12 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Marchant, R., Ramos, F. & Sanner, S.. (2014). Sequential Bayesian Optimisation for Spatial-Temporal Monitoring. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:7-16 Available from https://proceedings.mlr.press/r12/marchant14a.html. Reissued by PMLR on 04 October 2026.

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