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Details of Grant 

EPSRC Reference: EP/S016473/1
Title: Shared Autonomy via Robust Task Planning and Argumentation (SHARPA)
Principal Investigator: Bernardini, Dr S
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Computer Science
Organisation: Royal Holloway, Univ of London
Scheme: Overseas Travel Grants (OTGS)
Starts: 08 October 2018 Ends: 07 March 2019 Value (£): 15,868
EPSRC Research Topic Classifications:
Artificial Intelligence Robotics & Autonomy
EPSRC Industrial Sector Classifications:
Aerospace, Defence and Marine Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
04 Sep 2018 EPSRC ICT Prioritisation Panel September 2018 Announced
Summary on Grant Application Form
The overarching objective of this project is to endow autonomous systems with advanced decision-making capabilities and collaboration skills. We aim to build artificial systems that, in real-world environments, are capable of reasoning about high-level goals specified by human operators and formulating, in collaboration with them, a course of actions to successfully achieve such goals. Strategic reasoning and fluid teaming are fundamental skills of cognitive systems: they are needed in a variety of situations, from day-to-day tasks such as assisting humans in household chores, to extreme missions, such as space exploration. The techniques that we propose are general and can be used to support both robotic systems and software agents. We choose disaster response operations where unmanned aerial vehicles (UAVs) assist emergency responders as our demonstration arena. In this domain, in fact, it is crucial for the UAVs to think strategically to pursue goals efficiently and to act in concert with the human operators who are ultimately in charge of critical decisions.

The primary objective of the project is broken down into two strands. The first is to equip artificial artefacts that operate in real-world settings with the ability to reason about themselves and the world around them to determine plans for achieving high-level goals efficiently and robustly. Planning is a key component of intelligence and one of the most traditional fields of artificial intelligence (AI). Planning has achieved impressive results in idealised settings where the world is deterministic, and actions are instantaneous. However, planning in real-world environments in which temporal constraints and uncertainty cannot be ignored remains very challenging. Currently, no single temporal planner exhibits strong performance and, at the same time, handles all the features needed to represent practical problems. This project aims to contribute to filling this gap. On the one hand, we will investigate how different representations of temporal planning problems impact the performances of existing planners and whether there is one representation that facilitates efficient and flexible reasoning. On the other hand, we will formulate efficient algorithms that support advanced features of temporal reasoning such as required concurrency, timed transitions and uncontrollable action durations.

The second strand of this project emerges from the observation that, in any complex real-world operations, artificial artefacts rarely operate in isolation from humans. For the humans and the agents to team up in a fluidly and trustworthy, it is crucial that the agents' decision-making is intelligible to the human operators and also receptive to inputs from them. In this project, we explore the idea that planning can play a pivotal role in achieving intelligibility in autonomous systems. We consider two different facets of intelligibility: ex-post intelligibility, or explainability, whereby the system can exhibit the information and the logic that it has used to arrive at its decisions; and ex-ante intelligibility, or transparency, whereby the system exposes how it operates to a human operator in such a way that the operator can intervene and negotiate with the system a different course of actions. We investigate how planning and computational argumentation can be blended to achieve both ex-post and ex-ante intelligibility. Argumentation refers to a set of techniques for evaluating claims by considering reasons for and against them through logical reasoning. Argumentation techniques based on planning will empower the agent with the capacity to exhibit arguments in support of its decisions as well as to negotiate with the operator a change in the plan if needed.

Providing advances in the planning and collaboration skills of autonomous systems would benefit research in planning, AI and robotics and, more crucially, promote their broad adoption in real-world contexts.
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