In swarm robotics, agents interact through local roles to solve complex tasks beyond an individual's ability. Even though swarms are capable of carrying out some operations without the need for human intervention, many safety-critical applications still call for human operators to control and monitor the swarm. There are novel challenges to effective Human-Swarm Interaction (HSI) that are only beginning to be addressed. Explainability is one factor that can facilitate effective and trustworthy HSI and improve the overall performance of Human-Swarm team. Explainability was studied across various Human-AI domains, such as Human-Robot Interaction and Human-Centered ML. However, it is still ambiguous whether explanations studied in Human-AI literature would be beneficial in Human-Swarm research and development. Furthermore, the literature lacks foundational research on the prerequisites for explainability requirements in swarm robotics, i.e., what kind of questions an explainable swarm is expected to answer, and what types of explanations a swarm is expected to generate. By surveying 26 swarm experts, we seek to answer these questions and identify challenges experts faced to generate explanations in Human-Swarm environments. Our work contributes insights into defining a new area of research of eXplainable Swarm (xSwarm) which looks at how explainability can be implemented and developed in swarm systems. This paper opens the discussion on xSwarm and paves the way for more research in the field.

Objectively quantifying the value of player actions in football (soccer) is a challenging problem. To date, studies in football analytics have mainly focused on the attacking side of the game, while there has been less work on event-driven metrics for valuing defensive actions (e.g., tackles and interceptions). Therefore in this paper, we use deep learning techniques to define a novel metric that values such defensive actions by studying the threat of passages of play that preceded them. By doing so, we are able to value defensive actions based on what they prevented from happening in the game. Our Defensive Action Expected Threat (DAxT) model has been validated using real-world event-data from the 2017/2018 and 2018/2019 English Premier League seasons, and we combine our model outputs with additional features to derive an overall rating of defensive ability for players. Overall, we find that our model is able to predict the impact of defensive actions allowing us to better value defenders using event-data.

In electricity markets, the choice of the right pricing regime is crucial for the utilities because the price they charge to their consumers, in anticipation of their demand in real-time, is a key determinant of their profits and ultimately their survival in competitive energy markets. Among the existing pricing regimes, in this paper, we consider ex-ante dynamic pricing schemes as (i) they help to address the peak demand problem (a crucial problem in smart grids), and (ii) they are transparent and fair to consumers as the cost of electricity can be calculated before the actual consumption. In particular, we propose an axiomatic framework that establishes the conceptual underpinnings of the class of ex-ante dynamic pricing schemes. We first propose five key axioms that reflect the criteria that are vital for energy utilities and their relationship with consumers. We then prove an impossibility theorem to show that there is no pricing regime that satisfies all the five axioms simultaneously. We also study multiple cost functions arising from various pricing regimes to examine the subset of axioms that they satisfy. We believe that our proposed framework in this paper is first of its kind to evaluate the class of ex-ante dynamic pricing schemes in a manner that can be operationalised by energy utilities.

Finding and tracking targets and events in a live video feed is important for many commercial applications, from CCTV surveillance used by police and security firms, to the rapid mapping of events from aerial imagery. However, descriptions of targets are typically provided in natural language by the end users, and interpreting these in the context of a live video stream is a complex task. Due to current limitations in artificial intelligence, especially vision, this task cannot be automated and instead requires human supervision. Hence, in this paper, we consider the use of real-time crowdsourcing to identify and track targets given by a natural language description. In particular we present a novel method for augmenting live video with a real-time crowd.

Smart meters are intended to allow suppliers electricity network technologies, collectively called to access detailed energy consumption data and, more the smart grid (US Department Of Energy 2003; Galvin importantly, provide network information, such as real-time and Yeager 2008; UK Department of Energy and Climate pricing (RTP) signals, to consumers in an attempt to better Change 2009). A major component of this future vision is control or reduce demand when electricity is expensive that of energy storage. In particular, there is potential seen or carbon intensive on the grid (Hammerstrom et al. 2008; in the widespread adoption of small scale consumer storage Smith 2010). Accordingly, we envisage that micro-storage devices (i.e., micro-storage), which would allow consumers will be controlled by autonomous software agents that will to store electricity when demand is low, in order for react to RTP signals to minimise their owner's costs (i.e., it to be used during peak loads (Bathurst and Strbac 2003; they are self-interested). In this vein, we note our recent Ramchurn et al. 2011a; Vytelingum et al. 2010). This technology work (Vytelingum et al. 2010) in which we showed that, has the added advantage that it requires no significant when acting purely selfishly, large numbers of micro-storage change in how home appliances are used, and thus allows agents can cause instability in the aggregate demand profile.

Our approach Multi-agent task allocation is an important and challenging yields significant reductions in both run-time and communication, problem, which involves deciding how to assign a set thereby increasing real-world applicability. of agents to a set of tasks, both of which may change over In more detail, in this paper we advance the state-ofthe-art time (i.e., it is a dynamic environment). Moreover, it is often in the following ways: first, we present a novel, necessary for heterogeneous agents to form teams (known as online domain pruning algorithm specifically tailored to coalitions) to complete certain tasks in the environment. In dynamic task allocation environments to reduce the number coalitions, agents can often complete tasks more efficiently of potential solutions that need to be considered.