Autonomous Behaviour and Learning in an Uncertain World
Lead Research Organisation:
University of Oxford
Department Name: Engineering Science
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
- University of Oxford (Lead Research Organisation)
- Defence Science and Technology Laboratory (Co-funder)
- Sellafield (United Kingdom) (Co-funder)
- Schlumberger (United Kingdom) (Co-funder)
- United Kingdom Space Agency (Co-funder)
- Network Rail (Co-funder)
- BAE Systems (United Kingdom) (Co-funder)
- Schlumberger Limited (Collaboration)
Publications
Lloyd, C
(2016)
Latent Point Process Allocation
in Latent Point Process Allocation
M Hoffman
(2014)
Modular Mechanisms for Bayesian Optimization
M Osborne
(2012)
Active learning of model evidence using Bayesian quadrature.
M.P. Deisenroth
(2013)
Gaussian processes for data-efficient learning in robotics and control
M.W. Hoffman
(2014)
Bayesian techniques for black box optimization in system identification
Michael Osborne (Author)
(2012)
Active learning of model evidence using Bayesian quadrature
Osborne, M.
(2013)
Active learning of model evidence using Bayesian quadrature
in Advances in Neural Information Processing Systems 26 (NIPS 2012), December 2012, Lake Tahoe, USA.
Papachristodoulou A
(2015)
Advances in computational Lyapunov analysis using sum-of-squares programming
in Discrete and Continuous Dynamical Systems - Series B
Peet M
(2012)
A Converse Sum of Squares Lyapunov Result With a Degree Bound
in IEEE Transactions on Automatic Control
Prescott T
(2014)
Layered decomposition for the model order reduction of timescale separated biochemical reaction networks
in Journal of Theoretical Biology
Prescott TP
(2015)
Quantification of Interactions between Dynamic Cellular Network Functionalities by Cascaded Layering.
in PLoS computational biology
Raman D
(2016)
On the performance of nonlinear dynamical systems under parameter perturbation
in Automatica
Raman DV
(2017)
Delineating parameter unidentifiabilities in complex models.
in Physical review. E
Schmidt G
(2012)
Frequency synchronization and phase agreement in Kuramoto oscillator networks with delays
in Automatica
Shahriari Bobak
(2014)
An Entropy Search Portfolio for Bayesian Optimization
in arXiv e-prints
Stephen Roberts (Co-Author)
(2012)
Towards optimization-based multi-agent collision avoidance under continuous stochastic dynamics
Stephen Roberts (Co-Author)
(2012)
ICML-12: Towards auction-based multi-agent collision avoidance under continuous stochastic dynamics
Valmorbida G
(2022)
State-Feedback Design for Nonlinear Saturating Systems
in IEEE Transactions on Automatic Control
Valmorbida G
(2017)
Nonlinear Static State Feedback for Saturated Linear Plants via a Polynomial Approach
in IEEE Transactions on Automatic Control
Villaverde AF
(2016)
Structural Identifiability of Dynamic Systems Biology Models.
in PLoS computational biology
Zhang X
(2015)
A real-time control framework for smart power networks: Design methodology and stability
in Automatica
Zhang X
(2015)
Improving the Performance of Network Congestion Control Algorithms
in IEEE Transactions on Automatic Control
| Description | 1) Sparse efficient sampling based on informatics criteria can provide stable control algorithms and enable scalable multi-agent coordination. 2) Control mechanisms may be learned from data without a physical mechanism known 3) Guarantees of stability may be derived for probabilistic control methods 4) Bayesian optimisation allows for rapid learning of unknown functions. |
| Exploitation Route | via existing industry partners industry partners & academic publication. |
| Sectors | Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Energy,Other |
| URL | http://www.robots.ox.ac.uk/~parg/aisp |
| Description | Our control models have been integrated into drilling simulation by the industrial partner Schlumberger. These models show how, using sparse data, we can effectively use AI techniques to improve reliable control model creation - offering formal some guarantees as well. The extensions of this work can be useful in many areas, from finance to autonomous vehicles. |
| First Year Of Impact | 2014 |
| Sector | Energy,Financial Services, and Management Consultancy |
| Impact Types | Economic |
| Title | active sampling for control systems |
| Description | sparse observations for active control |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2013 |
| Provided To Others? | No |
| Description | Collaboration with Schlumberger |
| Organisation | Schlumberger Limited |
| Department | Schlumberger Cambridge Research |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | working closely to ensure industrial relevance and disseminate materials |
| Collaborator Contribution | providing data and expertise - funding two studentships as a knock on from this project |
| Impact | papers, software and know-how |
| Start Year | 2012 |