13,565 research outputs found

    Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work

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    The increasing prevalence of Artificial Intelligence (AI) in safety-critical contexts such as air-traffic control leads to systems that are practical and efficient, and to some extent explainable to humans to be trusted and accepted. The present structured literature analysis examines n = 236 articles on the requirements for the explainability and acceptance of AI. Results include a comprehensive review of n = 48 articles on information people need to perceive an AI as explainable, the information needed to accept an AI, and representation and interaction methods promoting trust in an AI. Results indicate that the two main groups of users are developers who require information about the internal operations of the model and end users who require information about AI results or behavior. Users' information needs vary in specificity, complexity, and urgency and must consider context, domain knowledge, and the user's cognitive resources. The acceptance of AI systems depends on information about the system's functions and performance, privacy and ethical considerations, as well as goal-supporting information tailored to individual preferences and information to establish trust in the system. Information about the system's limitations and potential failures can increase acceptance and trust. Trusted interaction methods are human-like, including natural language, speech, text, and visual representations such as graphs, charts, and animations. Our results have significant implications for future human-centric AI systems being developed. Thus, they are suitable as input for further application-specific investigations of user needs

    Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers

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    Real-time learning is crucial for robotic agents adapting to ever-changing, non-stationary environments. A common setup for a robotic agent is to have two different computers simultaneously: a resource-limited local computer tethered to the robot and a powerful remote computer connected wirelessly. Given such a setup, it is unclear to what extent the performance of a learning system can be affected by resource limitations and how to efficiently use the wirelessly connected powerful computer to compensate for any performance loss. In this paper, we implement a real-time learning system called the Remote-Local Distributed (ReLoD) system to distribute computations of two deep reinforcement learning (RL) algorithms, Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO), between a local and a remote computer. The performance of the system is evaluated on two vision-based control tasks developed using a robotic arm and a mobile robot. Our results show that SAC's performance degrades heavily on a resource-limited local computer. Strikingly, when all computations of the learning system are deployed on a remote workstation, SAC fails to compensate for the performance loss, indicating that, without careful consideration, using a powerful remote computer may not result in performance improvement. However, a carefully chosen distribution of computations of SAC consistently and substantially improves its performance on both tasks. On the other hand, the performance of PPO remains largely unaffected by the distribution of computations. In addition, when all computations happen solely on a powerful tethered computer, the performance of our system remains on par with an existing system that is well-tuned for using a single machine. ReLoD is the only publicly available system for real-time RL that applies to multiple robots for vision-based tasks.Comment: Appears in Proceedings of the 2023 International Conference on Robotics and Automation (ICRA). Source code at https://github.com/rlai-lab/relod and companion video at https://youtu.be/7iZKryi1xS

    CARLA+: An Evolution of the CARLA Simulator for Complex Environment Using a Probabilistic Graphical Model

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    In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has the potential to improve road safety and traffic efficiency. However, an obvious challenge in this regard is how to define, model, and simulate the environment that captures the dynamics of a complex and urban environment. Therefore, in this research, we first define the dynamics of the envisioned environment, where we capture the dynamics relevant to the complex urban environment, specifically, highlighting the challenges that are unaddressed and are within the scope of collaborative autonomous driving. To this end, we model the dynamic urban environment leveraging a probabilistic graphical model (PGM). To develop the proposed solution, a realistic simulation environment is required. There are a number of simulators—CARLA (Car Learning to Act), one of the prominent ones, provides rich features and environment; however, it still fails on a few fronts, for example, it cannot fully capture the complexity of an urban environment. Moreover, the classical CARLA mainly relies on manual code and multiple conditional statements, and it provides no pre-defined way to do things automatically based on the dynamic simulation environment. Hence, there is an urgent need to extend the off-the-shelf CARLA with more sophisticated settings that can model the required dynamics. In this regard, we comprehensively design, develop, and implement an extension of a classical CARLA referred to as CARLA+ for the complex environment by integrating the PGM framework. It provides a unified framework to automate the behavior of different actors leveraging PGMs. Instead of manually catering to each condition, CARLA+ enables the user to automate the modeling of different dynamics of the environment. Therefore, to validate the proposed CARLA+, experiments with different settings are designed and conducted. The experimental results demonstrate that CARLA+ is flexible enough to allow users to model various scenarios, ranging from simple controlled models to complex models learned directly from real-world data. In the future, we plan to extend CARLA+ by allowing for more configurable parameters and more flexibility on the type of probabilistic networks and models one can choose. The open-source code of CARLA+ is made publicly available for researchers

    Inverse estimation of the transfer velocity of money

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    Monitoring the money supply is an important prerequisite for conducting sound monetary policy, yet monetary indicators are conventionally estimated in aggregate. This paper proposes a new methodology that is able to leverage micro-level transaction data from real-world payment systems. We apply a novel computational technique to measure the durations for which money is held in individual accounts, and compute the transfer velocity of money from its inverse. Our new definition reduces to existing definitions under conventional assumptions. However, inverse estimation remains suitable for payment systems where the total balance fluctuates and spending patterns change in time. Our method is applied to study Sarafu, a small digital community currency in Kenya, where transaction data is available from 25 January 2020 to 15 June 2021. We find that the transfer velocity of Sarafu was higher than it would seem, in aggregate, because not all units of Sarafu remained in active circulation. Moreover, inverse estimation reveals strong heterogineities and enables comparisons across subgroups of spenders. Some units of Sarafu were held for minutes, others for months, and spending patterns differed across communities using Sarafu. The rate of circulation and the effective balance of Sarafu changed substantially over time, as these communities experienced economic disruptions related to the COVID-19 pandemic and seasonal food insecurity. These findings contribute to a growing body of literature documenting the heterogeneous patterns underlying headline macroeconomic indicators and their relevance for policy. Inverse estimation may be especially useful in studying the response of spenders to targeted monetary operations

    Visual Programming Paradigm for Organizations in Multi-Agent Systems

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    Over the past few years, due to a fast digitalization process, business activities witnessed the adoption of new technologies, such as Multi-Agent Systems, to increase the autonomy of their activities. However, the complexity of these technologies often hinders the capability of domain experts, who do not possess coding skills, to exploit them directly. To take advantage of these individuals' expertise in their field, the idea of a user-friendly and accessible Integrated Development Environment arose. Indeed, efforts have already been made to develop a block-based visual programming language for software agents. Although the latter project represents a huge step forward, it does not provide a solution for addressing complex, real-world use cases where interactions and coordination among single entities are crucial. To address this problem, Multi-Agent Oriented Programming introduces organization as a first-class abstraction for designing and implementing Multi-Agent Systems. Therefore, this thesis aims to provide a solution allowing users to impose an organization on top of the agents easily. Since ease of use and intuitiveness remain the key points for this project, users will be able to define organizations through visual language and an intuitive development environment

    Outsourcing the business of development : the rise of for-profit consultancies in the UK Aid Sector

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    Funding: Economic and Social Research Council - ES/V01269X/1.While much attention has been paid to the ways in which the private sector is now embedded within the field of development, one group of actors — for-profit development consultancies and contractors, or service providers — has received relatively little attention. This article analyses the growing role of for-profit consultancies and contractors in British aid delivery, which has been driven by two key trends: first, the outsourcing of managerial, audit and knowledge-management functions as part of efforts to bring private sector approaches and skills into public spending on aid; and second, the reconfiguration of aid spending towards markets and the private sector, and away from locally embedded, state-focused aid programming. The authors argue that both trends were launched under New Labour in the early 2000s, and super-charged under successive Conservative governments. The resulting entanglement means that the policies and practices of the UK government's aid agencies, and the interests and forms of for-profit service providers, are increasingly mutually constitutive. Amongst other implications, this shift acts to displace traditional forms of contestation and accountability of aid delivery.Publisher PDFPeer reviewe

    Statistical characterisation of public AC EV chargers in the UK

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    In recent years, the public AC electric vehicle (EV) charging network in the United Kingdom (UK) has experienced significant growth, more than doubling in size. However, there remains a significant lack of information regarding usage patterns, which hampers decision-making for future infrastructure planning. This study addresses this gap by presenting a statistical analysis based on data from nearly twelve thousand EV charging sessions. The data was collected from 595 AC charging sockets, with 85% operating at 7 kW and the remaining 15% at 22 kW, throughout the UK between April 2022 and July 2022. The analysis focuses on key factors that define the primary characteristics of the current public EV charging ecosystem, including utilisation rates, arrival-departure times, sojourn durations, energy transfer, and overstay durations. Several important observations are made, such as the variability in utilisation rates, factors influencing overstay periods, and peak demand periods. With two case studies, the potential role of smart charging in leveraging EV flexibility is shown by lowering and shifting the peak EV loads. The findings of this study have significant implications for the planning and efficient allocation of investments to expand the charging infrastructure. By gaining a better understanding of the current charging ecosystem, informed decisions can be made to optimize the usage and expansion of EV charging facilities

    Learning to Collaborate by Grouping: a Consensus-oriented Strategy for Multi-agent Reinforcement Learning

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    Multi-agent systems require effective coordination between groups and individuals to achieve common goals. However, current multi-agent reinforcement learning (MARL) methods primarily focus on improving individual policies and do not adequately address group-level policies, which leads to weak cooperation. To address this issue, we propose a novel Consensus-oriented Strategy (CoS) that emphasizes group and individual policies simultaneously. Specifically, CoS comprises two main components: (a) the vector quantized group consensus module, which extracts discrete latent embeddings that represent the stable and discriminative group consensus, and (b) the group consensus-oriented strategy, which integrates the group policy using a hypernet and the individual policies using the group consensus, thereby promoting coordination at both the group and individual levels. Through empirical experiments on cooperative navigation tasks with both discrete and continuous spaces, as well as Google research football, we demonstrate that CoS outperforms state-of-the-art MARL algorithms and achieves better collaboration, thus providing a promising solution for achieving effective coordination in multi-agent systems

    Interactive visualizations of unstructured oceanographic data

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    The newly founded company Oceanbox is creating a novel oceanographic forecasting system to provide oceanography as a service. These services use mathematical models that generate large hydrodynamic data sets as unstructured triangular grids with high-resolution model areas. Oceanbox makes the model results accessible in a web application. New visualizations are needed to accommodate land-masking and large data volumes. In this thesis, we propose using a k-d tree to spatially partition unstructured triangular grids to provide the look-up times needed for interactive visualizations. A k-d tree is implemented in F# called FsKDTree. This thesis also describes the implementation of dynamic tiling map layers to visualize current barbs, scalar fields, and particle streams. The current barb layer queries data from the data server with the help of the k-d tree and displays it in the browser. Scalar fields and particle streams are implemented using WebGL, which enables the rendering of triangular grids. Stream particle visualization effects are implemented as velocity advection computed on the GPU with textures. The new visualizations are used in Oceanbox's production systems, and spatial indexing has been integrated into Oceanbox's archive retrieval system. FsKDTree improves tree creation times by up to 4x over the C# equivalent and improves search times by up to 13x compared to the .NET C# implementation. Finally, the largest model areas can be viewed with current barbs, scalar fields, and particle stream visualizations at 60 FPS, even for the largest model areas provided by the service
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