Negotiation is essential in settings where autonomous agents have conflicting interests and a desire to cooperate. For this reason, mechanisms in which agents exchange potential agreements according to various rules of interaction have become very popular in recent years as evident, for example, in the auction and mechanism design community. However, a growing body of research is now emerging which points out limitations in such mechanisms and advocates the idea that agents can increase the likelihood and quality of an agreement by exchanging arguments which influence each others' states. This community further argues that argument exchange is sometimes essential when various assumptions about agent rationality cannot be satisfied. To this end, in this article, we identify the main research motivations and ambitions behind work in the field. We then provide a conceptual framework through which we outline the core elements and features required by agents engaged in argumentation-based negotiation, as well as the environment that hosts these agents. For each of these elements, we survey and evaluate existing proposed techniques in the literature and highlight the major challenges that need to be addressed if argument-based negotiation research is to reach its full potential.

Coalition formation is a fundamental type of interaction that involves the creation of coherent groupings of distinct, autonomous, agents in order to efficiently achieve their individual or collective goals. Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of determining which of the many possible coalitions to form in order to achieve some goal. This usually requires calculating a value for every possible coalition, known as the coalition value, which indicates how beneficial that coalition would be if it was formed. Once these values are calculated, the agents usually need to find a combination of coalitions, in which every agent belongs to exactly one coalition, and by which the overall outcome of the system is maximized. However, this coalition structure generation problem is extremely challenging due to the number of possible solutions that need to be examined, which grows exponentially with the number of agents involved. To date, therefore, many algorithms have been proposed to solve this problem using different techniques ranging from dynamic programming, to integer programming, to stochastic search all of which suffer from major limitations relating to execution time, solution quality, and memory requirements. With this in mind, we develop an anytime algorithm to solve the coalition structure generation problem. Specifically, the algorithm uses a novel representation of the search space, which partitions the space of possible solutions into sub-spaces such that it is possible to compute upper and lower bounds on the values of the best coalition structures in them. These bounds are then used to identify the sub-spaces that have no potential of containing the optimal solution so that they can be pruned. The algorithm, then, searches through the remaining sub-spaces very efficiently using a branch-and-bound technique to avoid examining all the solutions within the searched subspace(s). In this setting, we prove that our algorithm enumerates all coalition structures efficiently by avoiding redundant and invalid solutions automatically. Moreover, in order to effectively test our algorithm we develop a new type of input distribution which allows us to generate more reliable benchmarks compared to the input distributions previously used in the field. Given this new distribution, we show that for 27 agents our algorithm is able to find solutions that are optimal in 0.175% of the time required by the fastest available algorithm in the literature. The algorithm is anytime, and if interrupted before it would have normally terminated, it can still provide a solution that is guaranteed to be within a bound from the optimal one. Moreover, the guarantees we provide on the quality of the solution are significantly better than those provided by the previous state of the art algorithms designed for this purpose. For example, for the worst case distribution given 25 agents, our algorithm is able to find a 90% efficient solution in around 10% of time it takes to find the optimal solution.

HACs offer a new science for exploring the computational and human aspects of society.

The sports domain presents a number of significant computational challenges for artificial intelligence (AI) and machine learning (ML). In this paper, we explore the techniques that have been applied to the challenges within team sports thus far. We focus on a number of different areas, namely match outcome prediction, tactical decision making, player investments, fantasy sports, and injury prediction. By assessing the work in these areas, we explore how AI is used to predict match outcomes and to help sports teams improve their strategic and tactical decision making. In particular, we describe the main directions in which research efforts have been focused to date. This highlights not only a number of strengths but also weaknesses of the models and techniques that have been employed. Finally, we discuss the research questions that exist in order to further the use of AI and ML in team sports.

Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy "in the wild" -- 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.

Emergency responders are faced with a number of significant challenges when managing major disasters. First, the number of rescue tasks posed is usually larger than the number of responders (or agents) and the resources available to them. Second, each task is likely to require a different level of effort in order to be completed by its deadline. Third, new tasks may continually appear or disappear from the environment, thus requiring the responders to quickly recompute their allocation of resources. Fourth, forming teams or coalitions of multiple agents from different agencies is vital since no single agency will have all the resources needed to save victims, unblock roads and extinguish the fires which might erupt in the disaster space. Given this, coalitions have to be efficiently selected and scheduled to work across the disaster space so as to maximize the number of lives and the portion of the infrastructure saved. In particular, it is important that the selection of such coalitions should be performed in a decentralized fashion in order to avoid a single point of failure in the system. Moreover, it is critical that responders communicate only locally given they are likely to have limited battery power or minimal access to long-range communication devices. Against this background, we provide a novel decentralized solution to the coalition formation process that pervades disaster management. More specifically, we model the emergency management scenario defined in the RoboCup Rescue disaster simulation platform as a coalition formation with spatial and temporal constraints (CFST) problem where agents form coalitions to complete tasks, each with different demands. To design a decentralized algorithm for CFST, we formulate it as a distributed constraint optimization problem and show how to solve it using the state-of-the-art Max-Sum algorithm that provides a completely decentralized message-passing solution. We then provide a novel algorithm (F-Max-Sum) that avoids sending redundant messages and efficiently adapts to changes in the environment. In empirical evaluations, our algorithm is shown to generate better solutions than other decentralized algorithms used for this problem.