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)
- BAE Systems (United Kingdom) (Co-funder)
- Network Rail (Co-funder)
- Schlumberger Limited (Collaboration)
Publications
Baker AP
(2014)
Fast transient networks in spontaneous human brain activity.
in eLife
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)
M Hoffman
(2014)
Modular Mechanisms for Bayesian Optimization
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
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
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 |