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)
- NETWORK RAIL LIMITED (Co-funder)
- Schlumberger Cambridge Research 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
M Hoffman
(2014)
Modular Mechanisms for Bayesian Optimization
Shahriari B
(2014)
An Entropy Search Portfolio for Bayesian Optimization
Calliess, J.
(2014)
Conservative collision prediction and avoidance for stochastic trajectories in continuous time and state.
in International Conference on Autonomous Agents and Multiagent Systems (AAMAS2014)
Ahmadi M
(2014)
Barrier Functionals for Output Functional Estimation of PDEs
Valmorbida G
(2015)
Bounds for Input- and State-to-Output Properties of Uncertain Linear Systems
Ahmadi M
(2015)
Barrier functionals for output functional estimation of PDEs
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
Prescott, Thomas P.
(2015)
Quantification of Interactions between Dynamic Cellular Network Functionalities by Cascaded Layering
F. Nyikosa
(2015)
Adaptive Bayesian Optimization for Online Portfolio Selection.
JM Hernandez-Lobato
(2015)
Predictive Enthropy Search for Bayesian Optimization with Unknown Constraints.
Prescott TP
(2015)
Quantification of Interactions between Dynamic Cellular Network Functionalities by Cascaded Layering.
in PLoS computational biology
Zhang X
(2015)
A real-time control framework for smart power networks: Design methodology and stability
in Automatica
Ahmadi M
(2015)
A convex approach to hydrodynamic analysis
Zhang X
(2015)
Improving the Performance of Network Congestion Control Algorithms
in IEEE Transactions on Automatic Control
Ahmadi M
(2015)
A Convex Approach to Hydrodynamic Analysis
Samo Y
(2015)
Generalized Spectral Kernels
Anderson J
(2015)
Advances in computational Lyapunov analysis using sum-of-squares programming
in Discrete and Continuous Dynamical Systems - Series B
Lloyd C
(2015)
Variational Inference for Gaussian Process Modulated Poisson Process
in Variational Inference for Gaussian Process Modulated Poisson Process
Villaverde AF
(2016)
Structural Identifiability of Dynamic Systems Biology Models.
in PLoS computational biology
| 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 |