Professor Maria Fox is a computer scientist and specialist in Automated Planning and Scheduling. Her particular research focus is on automating the strategic-level decision-making for actuated systems that operate in the physical environment. With Scott Hosking she co-leads the BAS AI Lab, bringing a combinatorial flavour of AI to the lab and broadening the range of AI applications that can be supported at BAS.

Before joining BAS, Maria was a senior scientist at Schlumberger Cambridge Research, developing automated planning and plan execution methods to improve consistency and reliability of onshore drilling operations. She holds a Royal Society Industry Fellowship working on automated campaign planning for the BAS fleet of underwater vehicles.

In her academic life, Maria held chairs in Computer Science, most recently at King’s College London and, before that, at the University of Strathclyde. During this period, she worked on fundamental methods and modelling languages for Automated Planning and developed some of the well-known reference planning systems for temporal planning. The methods that Maria has developed are fully generic and she has used them in a wide variety of applications, from planning power network management, battery loading and drilling operations, to planning search and track operations to find lost and evasive objects.

Key publications in Planning

Applications:

Michael Cashmore, Maria Fox, Derek Long, Daniele Magazzeni, Bram Ridder:
Opportunistic Planning in Autonomous Underwater Missions. IEEE Trans Autom. Sci. Eng. 15(2): 519-530 (2018)

Sara Bernardini, Maria Fox, Derek Long:
Combining temporal planning with probabilistic reasoning for autonomous surveillance missions. Auton. Robots 41(1): 181-203 (2017)

Chiara Piacentini, Varvara Alimisis, Maria Fox, Derek Long:
An extension of metric temporal planning with application to AC voltage control. Artif. Intell. 229: 210-245 (2015)

Sara Bernardini, Maria Fox, Derek Long:
Planning the Behaviour of Low-Cost Quadcopters for Surveillance Missions. ICAPS 2014

Daniele Magazzeni, Frédéric Py, Maria Fox, Derek Long, Kanna Rajan:
Policy learning for autonomous feature tracking. Auton. Robots 37(1): 47-69 (2014)

Maria Fox, Derek Long, Daniele Magazzeni:
Plan-based Policies for Efficient Multiple Battery Load Management. J. Artif. Intell. Res. 44: 335-382 (2012)

Planning methods and modelling:

Amanda Jane Coles, Andrew Coles, Maria Fox, Derek Long:
A Hybrid LP-RPG Heuristic for Modelling Numeric Resource Flows in Planning. J. Artif. Intell. Res. 46: 343-412 (2013)

 Amanda Jane Coles, Andrew Coles, Maria Fox, Derek Long:
COLIN: Planning with Continuous Linear Numeric Change. J. Artif. Intell. Res. 44: 1-96 (2012)

Amanda Jane Coles, Andrew Coles, Maria Fox, Derek Long:
Forward-Chaining Partial-Order Planning. ICAPS 2010: 42-49

Maria Fox, Derek Long:
Modelling Mixed Discrete-Continuous Domains for Planning. J. Artif. Intell. Res. 27: 235-297 (2006)

Maria Fox, Derek Long:
PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains. J. Artif. Intell. Res. 20: 61-124 (2003)

Previous Career:

1989: PhD in Computer Science: Abstraction methods for Planning

1989-1996: Lecturer in Computer Science, University College London

1996-2003: Lecturer (then Reader) in Computer Science, University of Durham

2003-2011: Professor in Computer Science (AI Planning), University of Strathclyde

2011-2016: Professor in Computer Science (AI Planning), King’s College London

2016-2021: Senior Scientist, Schlumberger Cambridge Research

Research interests

My research specialises in automated planning, scheduling and optimisation, and their application to real world cyber systems and robotics.

 

Collaborations

Planning and Autonomy Group, Schlumberger Cambridge Research

1. Digital Twins of the Polar Regions

   This project is developing digital twins of Antarctic and Arctic environments and resources. A digital twin makes it possible to test “what if” questions far more quickly than traditional computer models.

   Read more of: Digital Twins of the Polar Regions

   

3. Logist AI for environmentally aware decision support

   AMOP is developing Artificial Intelligence methods that aim to optimise the efficiency of Antarctic field operations, while maximising science delivery

   Read more of: Logist AI for environmentally aware decision support

   

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Royal Society Industry Fellowship: Command and Control for Oceanographic Survey

 

1. AI tool to revolutionise polar ship navigation

   Artificial Intelligence (AI) will enable ships navigating in polar ocean conditions to be more efficient using a new route planning tool created by British Antarctic Survey (BAS) researchers. The tool […]

   Read more of: AI tool to revolutionise polar ship navigation