Autonomous Behaviour and Learning in an Uncertain World
Lead Research Organisation:
University of Oxford
Department Name: Engineering Science
Abstract
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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
Valmorbida G
(2015)
Bounds for Input- and State-to-Output Properties of Uncertain Linear Systems
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.
Lloyd C
(2015)
Variational Inference for Gaussian Process Modulated Poisson Process
in Variational Inference for Gaussian Process Modulated Poisson Process
Ahmadi M
(2017)
Safety verification for distributed parameter systems using barrier functionals
in Systems & Control Letters
Kim Samo, Y. L
(2016)
String and Membrane Gaussian Process
in String and Membrane Gaussian Process
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
Villaverde AF
(2016)
Structural Identifiability of Dynamic Systems Biology Models.
in PLoS computational biology
Prescott TP
(2015)
Quantification of Interactions between Dynamic Cellular Network Functionalities by Cascaded Layering.
in PLoS computational biology
Raman DV
(2017)
Delineating parameter unidentifiabilities in complex models.
in Physical review. E
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 |