Shared Autonomy via Robust Task Planning and Argumentation (SHARPA)

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.

The goal of this project is to equip artificial artefacts, such as robots and software agents, with the ability to autonomously undertake sophisticated tasks and, when needed, to collaborate with humans in a natural and mutual intelligible way.

The potential for society and economy of robotics and autonomous systems is well understood and has been recently highlighted by many studies. The report "Disruptive technologies" by McKinsey identifies in advanced robotics and autonomous systems two of the main technologies that "have the potential to truly reshape the world in which we live and work" and estimates that advanced robotics could generate a potential impact of $1.9 - 6.4 trillion per year by 2025. The Royal Society report "Robotics and autonomous systems" states: "Robotics and autonomous are of immense societal impact, pervading all areas of society including medicine, transport, and manufacturing."

The use of both software agents and robotic artefacts in everyday life has risen sharply, and we see increasingly more examples of their use in society with several commercial products already on the market. As interaction with humans increases so does the demand for sophisticated capabilities associated with deliberation and high-level cognitive functions. WP1 of my proposal responds to this need and deals with endowing artificial systems with higher level cognitive functions that enable them to reason and act in complex environments. Planning is, in fact, considered central in the spectrum of capabilities required for autonomy.

The Royal Society report also states: "Fully autonomous robotics can be problematic. As a result, there is a shift from isolated decision-making systems to those that share control, with significant autonomy devolved to robotics and autonomous systems, leaving end-users to make only high-level decisions. Shared autonomy will demand the closing of the semantic gap between human and machine." WP2 of my proposal deals with shared autonomy and aims at contributing to close "gap between human and machine" by combining planning and argumentation to give rise to fluid and transparent teaming between human operators and artificial artefacts. The use of these two techniques will empower the human part with the ability to enquire into the behaviour of the machine and directly argue with it to better understand its line of reasoning and change it, if appropriate.

The need for an intelligible AI has gained a lot of traction in academia, industry and the public sector recently, and has been highlighted by several official reports. The House of Lords report "AI in the UK: ready, willing and able?" states: "We believe that the development of intelligible AI systems is a fundamental necessity if AI is to become an integral and trusted tool in our society." The techniques at the core of this proposal will help in achieving more intelligible autonomous agents, impacting a variety of AI artefacts, from virtual characters that interact with users online to autonomous cars to service robots that assist people at home.

WP3 deals with an application of clear importance for society: supporting disaster response operations via autonomous UAVs. Humanitarian disasters cost lives and can cause huge setbacks. The UK Government's Humanitarian Policy emphases how the use of innovative technology can have a considerable impact on improving the efficiency and reach of humanitarian assistance. When a disaster strikes, UAVs can provide support with risk assessment, mapping, planning and search-and-rescue in the affected region. Currently, two of the main barriers to the use of UAVs for disaster relief are that the availability of expert pilots to teleoperate them and the lack of transparency in the behaviour of these UAVs. This project deals with both these problems as it aims to develop autonomous UAVs that do not require to be continuously piloted and whose conduct is decided in concert with the human operators.