Autonomous Behaviour and Learning in an Uncertain World

Lead Research Organisation: University of Oxford
Department Name: Engineering Science


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.
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

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