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Details of Grant 

EPSRC Reference: EP/P008410/1
Title: AI Planning with Continuous Non-Linear Change
Principal Investigator: Coles, Dr A
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Informatics
Organisation: Kings College London
Scheme: First Grant - Revised 2009
Starts: 01 January 2017 Ends: 30 June 2018 Value (£): 100,748
EPSRC Research Topic Classifications:
Artificial Intelligence Robotics & Autonomy
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
19 Jul 2016 EPSRC ICT Prioritisation Panel - Jul 2016 Announced
Summary on Grant Application Form
Intelligent autonomous systems have a significant role to play in meeting the increasing needs of a growing modern society. These systems can take many forms. Autonomous robots can assist humans in performing tasks, work in manufacturing, and play an important role in cleaning up or exploring environments too hostile for humans. Autonomous large scale software systems can control our over-subscribed transport and power networks, allowing us to operate them as efficiently as possible to serve the growing population.

In order for an autonomous system to act intelligently and achieve its goals, it needs to be able to plan, that is decide what actions to take and when to take them: this is the problem of Artificial Intelligence (AI) planning. The major research goal for AI planning is to create systems that are domain-independent, that is they are not human-programmed to solve one specific problem; but rather are general purpose and capable of planning in scenarios encountered across a wide range of applications.

Decades of research has produced increasingly capable AI planners, and there have been successes in using these in a diverse range of applications, including the planning of global ocean liner movements and security penetration testing. There are, however, still major challenges to be met in creating systems that are scalable and expressive enough to form the core of the AI systems needed to meet future societal challenges. One major challenge, and the focus of this project, is equipping planners with the expressive capability to reason about a complex and dynamic world. Many interesting target problems, such as nuclear clean up or traffic control, require not only conventional reasoning based on facts that are true and false; but also reasoning about non-linear continuous dynamics: radiation exposure or traffic flow.

This project addresses the challenge of creating a planner capable of reasoning with non-linear continuous dynamics alongside all the existing state-of-the-art capabilities of the most expressive modern planners (time, deadlines, soft constraints and cost optimisation) without significantly compromising scalability. Such a system will be an invaluable asset in controlling the autonomous systems of the future.

The research challenges that need to be addressed to achieve this are significant, as present techniques for reasoning with these problems make compromises in one way or another. Some techniques are limited in scalability due to a requirement to 'discretise' time, splitting it into small chunks, and reasoning about whether to do something every fraction of a second. Others are incompatible with other expressive features; or rely on technologies that support only linear change. In this project we build on OPTIC, a planner that supports only linear change, due to its support of other expressive features and good potential for scalability.

We will address the challenges of reasoning with non-linear change in a linear framework by reasoning with piecewise-linear approximations of the continuous change. The main challenges here are determining how to integrate reasoning about these with existing techniques for expressive reasoning; and generating sufficiently accurate approximations automatically. The finer we make the approximation, the more points we have to reason about and the harder it is to solve efficiently; yet approximations that are not fine enough will not permit us to solve the problem.

Throughout the project, alongside development of the planner, we will focus on creating models of target problems, guided by our contacts with organisations in the relevant fields. These will allow us to ensure our work remains focussed on addressing the challenges that will most benefit application as well as providing us with benchmarks against which we can evaluate the project. Our target applications include nuclear clean up; medical dose scheduling and traffic flow management.
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