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
Prescott TP
(2015)
Quantification of Interactions between Dynamic Cellular Network Functionalities by Cascaded Layering.
in PLoS computational biology
Papachristodoulou A
(2015)
Advances in computational Lyapunov analysis using sum-of-squares programming
in Discrete and Continuous Dynamical Systems - Series B
Zhang X
(2015)
Improving the Performance of Network Congestion Control Algorithms
in IEEE Transactions on Automatic Control
Kom Samo, Y L
(2015)
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes
in Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes
Lloyd C
(2015)
Variational Inference for Gaussian Process Modulated Poisson Process
in Variational Inference for Gaussian Process Modulated Poisson Process
F. Nyikosa
(2015)
Adaptive Bayesian Optimization for Online Portfolio Selection.
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 |