144 research outputs found
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning
In this paper, we propose a novel model-based multi-agent reinforcement
learning approach named Value Decomposition Framework with Disentangled World
Model to address the challenge of achieving a common goal of multiple agents
interacting in the same environment with reduced sample complexity. Due to
scalability and non-stationarity problems posed by multi-agent systems,
model-free methods rely on a considerable number of samples for training. In
contrast, we use a modularized world model, composed of action-conditioned,
action-free, and static branches, to unravel the environment dynamics and
produce imagined outcomes based on past experience, without sampling directly
from the real environment. We employ variational auto-encoders and variational
graph auto-encoders to learn the latent representations for the world model,
which is merged with a value-based framework to predict the joint action-value
function and optimize the overall training objective. We present experimental
results in Easy, Hard, and Super-Hard StarCraft II micro-management challenges
to demonstrate that our method achieves high sample efficiency and exhibits
superior performance in defeating the enemy armies compared to other baselines.Comment: 14 page
Operational Research: methods and applications
This is the final version. Available on open access from Taylor & Francis via the DOI in this recordThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes
Jahrbuch Migration und Gesellschaft / Yearbook Migration and Society 2022/2023. Vol. 4, Climate
Migration is not a state of emergency, but a basic existential experience of humanity. It shapes contemporary societies by challenging established orders, creating transnational spaces beyond national hegemonies, creating new economies, influencing urban and communal ways of life, making inequality and precariousness visible locally and globally. Migration research as a social science does not narrow the focus to 'the migrants', but investigates the conditions for living together and shaping life between ethnicization and pluralization, discrimination and empowerment, division and participation. The Yearbook Migration and Society repeatedly turns the prism of narrative anew. The 2022/2023 edition focuses on the topic "Climate"
Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023
Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida
Operational research:methods and applications
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order
Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses
The ongoing deployment of the fifth generation (5G) wireless networks
constantly reveals limitations concerning its original concept as a key driver
of Internet of Everything (IoE) applications. These 5G challenges are behind
worldwide efforts to enable future networks, such as sixth generation (6G)
networks, to efficiently support sophisticated applications ranging from
autonomous driving capabilities to the Metaverse. Edge learning is a new and
powerful approach to training models across distributed clients while
protecting the privacy of their data. This approach is expected to be embedded
within future network infrastructures, including 6G, to solve challenging
problems such as resource management and behavior prediction. This survey
article provides a holistic review of the most recent research focused on edge
learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the
existing surveys on machine learning for 6G IoT security and machine
learning-associated threats in three different learning modes: centralized,
federated, and distributed. Then, we provide an overview of enabling emerging
technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of
existing research on attacks against machine learning and classify threat
models into eight categories, including backdoor attacks, adversarial examples,
combined attacks, poisoning attacks, Sybil attacks, byzantine attacks,
inference attacks, and dropping attacks. In addition, we provide a
comprehensive and detailed taxonomy and a side-by-side comparison of the
state-of-the-art defense methods against edge learning vulnerabilities.
Finally, as new attacks and defense technologies are realized, new research and
future overall prospects for 6G-enabled IoT are discussed
Formal Modelling for Multi-Robot Systems Under Uncertainty
Purpose of Review: To effectively synthesise and analyse multi-robot
behaviour, we require formal task-level models which accurately capture
multi-robot execution. In this paper, we review modelling formalisms for
multi-robot systems under uncertainty, and discuss how they can be used for
planning, reinforcement learning, model checking, and simulation.
Recent Findings: Recent work has investigated models which more accurately
capture multi-robot execution by considering different forms of uncertainty,
such as temporal uncertainty and partial observability, and modelling the
effects of robot interactions on action execution. Other strands of work have
presented approaches for reducing the size of multi-robot models to admit more
efficient solution methods. This can be achieved by decoupling the robots under
independence assumptions, or reasoning over higher level macro actions.
Summary: Existing multi-robot models demonstrate a trade off between
accurately capturing robot dependencies and uncertainty, and being small enough
to tractably solve real world problems. Therefore, future research should
exploit realistic assumptions over multi-robot behaviour to develop smaller
models which retain accurate representations of uncertainty and robot
interactions; and exploit the structure of multi-robot problems, such as
factored state spaces, to develop scalable solution methods.Comment: 23 pages, 0 figures, 2 tables. Current Robotics Reports (2023). This
version of the article has been accepted for publication, after peer review
(when applicable) but is not the Version of Record and does not reflect
post-acceptance improvements, or any corrections. The Version of Record is
available online at: https://dx.doi.org/10.1007/s43154-023-00104-
Operational Research: Methods and Applications
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes
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