Machine Learning and Adaptation of Domain Models to Support Real-Time Planning in Autonomous Systems
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Informatics
Abstract
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Organisations
- University of Edinburgh (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
Wickler G
(2014)
Creating Planning Domain Models in KEWI
Wickler G
(2014)
KEWI - A Knowledge Engineering Tool for Modelling AI Planning Tasks
Wickler G
(2015)
Knowledge Discovery, Knowledge Engineering and Knowledge Management
McCluskey, T. L.
(2016)
Learning Action Models for Planning: An Overview of the Hedlamp Project
Tait J
(2017)
The Virtual Oil Rig - Simulation-based Immersive Training
in Virtual Education Journal
Title | Oil Rig for Simulation and Training in Virtual Reality |
Description | A realistic Oil Rig 3D mesh model used by Robert Gordon University (RGU) Oil & Gas Centre in Aberdeen for training purposes and as an educational resource has been provided in the Virtual University of Edinburgh (Vue) hosted OpenSimulator-based OSGrid "Oil Rig" region, as well as on other OpenSimulator-based grids hosted at Edinburgh. The model includes a seabed Blow Out Preventer (BOP) of the type being studied as an application area for emergency standard operating procedure (SOP) automation within the UK EPSRC supported HedLAMP Project (Grant EP/J011800/1) and with the collaboration of Schlumberger Cambridge Ltd. The Oil Rig region was also shown in immersive 3D virtual reality using an Oculus Rift DK2 head mounted display (HMD) to the HEdLAMP project members at their meeting in Edinburgh on 28th November 2014. |
Type Of Art | Artefact (including digital) |
Year Produced | 2014 |
Impact | Creating awareness of virtual reality and virtual worlds for realistic simulation and emergency procedure training purposes. |
URL | http://blog.inf.ed.ac.uk/atate/2014/11/28/oil-rig-in-virtual-reality/ |
Description | Artificial Intelligence plan representations and planning methods can be used to model complex processes that are highly critical to product and process safety. Examples of this have been demonstrated in oil well blow out preventer emergency procedures. A Massive Open Online Course (MOOC) in Artificial Intelligence Planning was created and presented three times on the Coursera Platform in 2013, 2014 and 2015 reaching over 113,000 registered students. It was used to brief collaborators on the project. The MOOC materials have also been made available under an open licence (CC-BY-NC) for self-guided study at any time via open educational resources at Edinburgh and on YouTube. |
Exploitation Route | Artificial Intelligence planning techniques are usable in many sectors and will work alongside other emerging AI methods to add capabilities to a wide range of systems. The AI Planning MOOC provides technical material to allow others to understand and make use of the techniques explored on this grant. |
Sectors | Aerospace Defence and Marine Agriculture Food and Drink Chemicals Communities and Social Services/Policy Construction Creative Economy Digital/Communication/Information Technologies (including Software) Education Electronics Energy Environment Financial Services and Management Consultancy Healthcare Leisure Activities including Sports Recreation and Tourism Government Democracy and Justice Manufacturing including Industrial Biotechology Culture Heritage Museums and Collections Pharmaceu |
URL | http://www.aiai.ed.ac.uk/project/hedlamp/ |
Description | To inform oil well and drilling emergency procedure specification and use. To raise awareness and knowledge about Artificial Intelligence Planning techniques (via MOOC). Also, please see the impact description provided for EP/J011991/1 |
First Year Of Impact | 2018 |
Sector | Aerospace, Defence and Marine,Agriculture, Food and Drink,Construction,Creative Economy,Education,Energy,Environment,Healthcare,Leisure Activities, including Sports, Recreation and Tourism,Culture, Heritage, Museums and Collections,Retail,Transport |
Impact Types | Cultural Societal Economic |
Title | H3 - Hybrid Hierarchical Heuristic Planner |
Description | A novel artificial intelligence planning tool based on a hybrid combination of hierarchical task network (HTN) and heuristic planning. |
Type Of Material | Improvements to research infrastructure |
Year Produced | 2015 |
Provided To Others? | Yes |
Impact | Initial release of a novel artificial intelligence planning tool based on a hybrid combination of hierarchical task network (HTN) and heuristic planning. Integrated as a module into Drupal. |
URL | http://www.aiai.ed.ac.uk/project/h3/ |
Description | AI Planning Applications with Schlumberger Cambridge Research |
Organisation | Schlumberger Limited |
Department | Schlumberger Cambridge Research |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Modelling and use of emergency procedures for blow out preventer (BOP) operations related to oil well operations. |
Collaborator Contribution | Provision of documents, procedures, diagrams and expert knowledge related to oil well operations and emergency procedures for such operations. |
Impact | Models of blow out preventer (BOP) emergency procedures, and demonstrations of the potential for AI plan representations and AI planning to be used in such processes. |
Start Year | 2012 |
Description | Artificial Intelligence Massive Open Online Course (MOOC) |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | Massive Open Online Course (MOOC) on topic of Artificial Intelligence Planning (AI Planning) created and run three times in 2013, 2014 and 2016 on the Coursera platform. Reaching 113,565 registered students who had a wide variety of background and came from approximately 120 countries. Instructors were Prof. Austin Tate and Dr. Gerhard Wickler. The MOOC materials were created in cooperation with six international guest lecturers on specialised topics. The MOOC materials have also been provided to be available on demand in future using a CC-BY-NC licence via the University of Edinburgh media servers, in Open Educational Resources facilities at Edinburgh and via YouTube. |
Year(s) Of Engagement Activity | 2013,2014,2015,2016,2017 |
URL | http://www.aiai.ed.ac.uk/project/plan/ooc/ |