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)
- DSTL Porton Down (Co-funder)
- UK Space Agency (UKSA) (Co-funder)
- BAE Systems Advanced Technology Centre (Co-funder)
- Schlumberger Limited (Collaboration)
Publications
Valmorbida G
(2022)
State-Feedback Design for Nonlinear Saturating Systems
in IEEE Transactions on Automatic Control
Zheng Y
(2018)
Scalable Design of Structured Controllers Using Chordal Decomposition
in IEEE Transactions on Automatic Control
Raman DV
(2017)
Delineating parameter unidentifiabilities in complex models.
in Physical review. E
Ahmadi M
(2017)
Safety verification for distributed parameter systems using barrier functionals
in Systems & Control Letters
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
Ahmadi M
(2016)
Dissipation inequalities for the analysis of a class of PDEs
in Automatica
Raman D
(2016)
Delineating Parameter Unidentifiabilities in Complex Models
Raman D
(2016)
On the performance of nonlinear dynamical systems under parameter perturbation
in Automatica
Lloyd, C
(2016)
Latent Point Process Allocation
in Latent Point Process Allocation
Kim Samo, Y. L
(2016)
String and Membrane Gaussian Process
in String and Membrane Gaussian Process
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
M.W. Hoffman
(2014)
Bayesian techniques for black box optimization in system identification
Prescott TP
(2014)
Layered decomposition for the model order reduction of timescale separated biochemical reaction networks.
in Journal of theoretical biology
Baker AP
(2014)
Fast transient networks in spontaneous human brain activity.
in eLife
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
Jan Calliess (Author)
(2013)
Multi-agent planning with mixed-integer programming and adaptive interaction constraint generation.
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.
Calliess, J.
(2013)
Nonlinear adaptive hybrid control by combining Gaussian process system identification with classical control laws.
in WS- Novel Methods for Learning and Optimization of Control Policies and Trajectories for Robotics, ICRA, 2013.
Hancock E
(2013)
Generalised absolute stability and sum of squares
in Automatica
M.P. Deisenroth
(2013)
Gaussian processes for data-efficient learning in robotics and 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 |