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)
- BAE Systems (United Kingdom) (Co-funder)
- Network Rail (Co-funder)
- Schlumberger Limited (Collaboration)
Publications
Peet M
(2012)
A Converse Sum of Squares Lyapunov Result With a Degree Bound
in IEEE Transactions on Automatic Control
Ahmadi M
(2015)
A Convex Approach to Hydrodynamic Analysis
Ahmadi M
(2015)
A convex approach to hydrodynamic analysis
Zhang X
(2015)
A real-time control framework for smart power networks: Design methodology and stability
in Automatica
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 Osborne
(2012)
Active learning of model evidence using Bayesian quadrature.
F. Nyikosa
(2015)
Adaptive Bayesian Optimization for Online Portfolio Selection.
Papachristodoulou A
(2015)
Advances in computational Lyapunov analysis using sum-of-squares programming
in Discrete and Continuous Dynamical Systems - Series B
Anghel M
(2013)
Algorithmic Construction of Lyapunov Functions for Power System Stability Analysis
in IEEE Transactions on Circuits and Systems I: Regular Papers
Shahriari Bobak
(2014)
An Entropy Search Portfolio for Bayesian Optimization
in arXiv e-prints
Ahmadi M
(2014)
Barrier Functionals for Output Functional Estimation of PDEs
Ahmadi M
(2015)
Barrier functionals for output functional estimation of PDEs
M.W. Hoffman
(2014)
Bayesian techniques for black box optimization in system identification
Valmorbida G
(2015)
Bounds for Input- and State-to-Output Properties of Uncertain Linear Systems
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)
Raman D
(2016)
Delineating Parameter Unidentifiabilities in Complex Models
Raman DV
(2017)
Delineating parameter unidentifiabilities in complex models.
in Physical review. E
Ahmadi M
(2016)
Dissipation inequalities for the analysis of a class of PDEs
in Automatica
Baker AP
(2014)
Fast transient networks in spontaneous human brain activity.
in eLife
Schmidt G
(2012)
Frequency synchronization and phase agreement in Kuramoto oscillator networks with delays
in Automatica
M.P. Deisenroth
(2013)
Gaussian processes for data-efficient learning in robotics and control
Hancock E
(2013)
Generalised absolute stability and sum of squares
in Automatica
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 |