2,517 research outputs found
Cyclic proof systems for modal fixpoint logics
This thesis is about cyclic and ill-founded proof systems for modal fixpoint logics, with and without explicit fixpoint quantifiers.Cyclic and ill-founded proof-theory allow proofs with infinite branches or paths, as long as they satisfy some correctness conditions ensuring the validity of the conclusion. In this dissertation we design a few cyclic and ill-founded systems: a cyclic one for the weak Grzegorczyk modal logic K4Grz, based on our explanation of the phenomenon of cyclic companionship; and ill-founded and cyclic ones for the full computation tree logic CTL* and the intuitionistic linear-time temporal logic iLTL. All systems are cut-free, and the cyclic ones for K4Grz and iLTL have fully finitary correctness conditions.Lastly, we use a cyclic system for the modal mu-calculus to obtain a proof of the uniform interpolation property for the logic which differs from the original, automata-based one
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
A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks
Transformer is a deep neural network that employs a self-attention mechanism
to comprehend the contextual relationships within sequential data. Unlike
conventional neural networks or updated versions of Recurrent Neural Networks
(RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in
handling long dependencies between input sequence elements and enable parallel
processing. As a result, transformer-based models have attracted substantial
interest among researchers in the field of artificial intelligence. This can be
attributed to their immense potential and remarkable achievements, not only in
Natural Language Processing (NLP) tasks but also in a wide range of domains,
including computer vision, audio and speech processing, healthcare, and the
Internet of Things (IoT). Although several survey papers have been published
highlighting the transformer's contributions in specific fields, architectural
differences, or performance evaluations, there is still a significant absence
of a comprehensive survey paper encompassing its major applications across
various domains. Therefore, we undertook the task of filling this gap by
conducting an extensive survey of proposed transformer models from 2017 to
2022. Our survey encompasses the identification of the top five application
domains for transformer-based models, namely: NLP, Computer Vision,
Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze
the impact of highly influential transformer-based models in these domains and
subsequently classify them based on their respective tasks using a proposed
taxonomy. Our aim is to shed light on the existing potential and future
possibilities of transformers for enthusiastic researchers, thus contributing
to the broader understanding of this groundbreaking technology
Stochastic Mathematical Systems
We introduce a framework that can be used to model both mathematics and human
reasoning about mathematics. This framework involves {stochastic mathematical
systems} (SMSs), which are stochastic processes that generate pairs of
questions and associated answers (with no explicit referents). We use the SMS
framework to define normative conditions for mathematical reasoning, by
defining a ``calibration'' relation between a pair of SMSs. The first SMS is
the human reasoner, and the second is an ``oracle'' SMS that can be interpreted
as deciding whether the question-answer pairs of the reasoner SMS are valid. To
ground thinking, we understand the answers to questions given by this oracle to
be the answers that would be given by an SMS representing the entire
mathematical community in the infinite long run of the process of asking and
answering questions. We then introduce a slight extension of SMSs to allow us
to model both the physical universe and human reasoning about the physical
universe. We then define a slightly different calibration relation appropriate
for the case of scientific reasoning. In this case the first SMS represents a
human scientist predicting the outcome of future experiments, while the second
SMS represents the physical universe in which the scientist is embedded, with
the question-answer pairs of that SMS being specifications of the experiments
that will occur and the outcome of those experiments, respectively. Next we
derive conditions justifying two important patterns of inference in both
mathematical and scientific reasoning: i) the practice of increasing one's
degree of belief in a claim as one observes increasingly many lines of evidence
for that claim, and ii) abduction, the practice of inferring a claim's
probability of being correct from its explanatory power with respect to some
other claim that is already taken to hold for independent reasons.Comment: 43 pages of text, 6 pages of references, 11 pages of appendice
Review of graph-based hazardous event detection methods for autonomous driving systems
Automated and autonomous vehicles are often required to operate in complex road environments with potential hazards that may lead to hazardous events causing injury or even death. Therefore, a reliable autonomous hazardous event detection system is a key enabler for highly autonomous vehicles (e.g., Level 4 and 5 autonomous vehicles) to operate without human supervision for significant periods of time. One promising solution to the problem is the use of graph-based methods that are powerful tools for relational reasoning. Using graphs to organise heterogeneous knowledge about the operational environment, link scene entities (e.g., road users, static objects, traffic rules) and describe how they affect each other. Due to a growing interest and opportunity presented by graph-based methods for autonomous hazardous event detection, this paper provides a comprehensive review of the state-of-the-art graph-based methods that we categorise as rule-based, probabilistic, and machine learning-driven. Additionally, we present an in-depth overview of the available datasets to facilitate hazardous event training and evaluation metrics to assess model performance. In doing so, we aim to provide a thorough overview and insight into the key research opportunities and open challenges
Subjectivity, nature, existence: Foundational issues for enactive phenomenology
This thesis explores and discusses foundational issues concerning the relationship between phenomenological philosophy and the enactive approach to cognitive science, with the aim of clarifying, developing, and promoting the project of enactive phenomenology. This project is framed by three general ideas: 1) that the sciences of mind need a phenomenological grounding, 2) that the enactive approach is the currently most promising attempt to provide mind science with such a grounding, and 3) that this attempt involves both a naturalization of phenomenology and a phenomenologization of the concept of nature. More specifically, enactive phenomenology is the project of pursuing mutually illuminative exchanges between, on the one hand, phenomenological investigations of the structures of lived experience and embodied existence and, on the other, scientific accounts of mind and life – in particular those framed by theories of biological self-organization. The thesis consists of two parts. Part one is an introductory essay that seeks to clarify some of enactive phenomenology’s overarching philosophical commitments by tracing some of its historical roots. Part two is a compilation of four articles, each of which intervenes in a different contemporary debate relevant to the dissertation’s project
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