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)
- Sellafield Limited (Co-funder)
- Schlumberger Cambridge Research Limited (Co-funder)
- NETWORK RAIL LIMITED (Co-funder)
- UK Space Agency (UKSA) (Co-funder)
- DSTL Porton Down (Co-funder)
- BAE Systems Advanced Technology Centre (Co-funder)
- Schlumberger Limited (Collaboration)
Publications
Prescott TP
(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
Peet M
(2012)
A Converse Sum of Squares Lyapunov Result With a Degree Bound
in IEEE Transactions on Automatic Control
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.
Michael Osborne (Author)
(2012)
Active learning of model evidence using Bayesian quadrature
M.W. Hoffman
(2014)
Bayesian techniques for black box optimization in system identification
M.P. Deisenroth
(2013)
Gaussian processes for data-efficient learning in robotics and control
M Osborne
(2012)
Active learning of model evidence using Bayesian quadrature.
M Hoffman
(2014)
Modular Mechanisms for Bayesian Optimization
Lloyd, C
(2016)
Latent Point Process Allocation
in Latent Point Process Allocation
| 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 |