584 research outputs found
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Transition 2.0: Re-establishing Constitutional Democracy in EU Member States
The central question of Transition 2.0 is this: what (and how) may a new government do to re-establish constitutional democracy, as well as repair membership within the European Union, without breaching the European rule of law? This volume demonstrates that EU law and international commitments impose constraints but also offer tools and assistance for facilitating the way back after rule of law and democratic backsliding. The various contributions explore the constitutional, legal, and social framework of 'Transition 2.0'.Dieser Band zeigt, dass das EU-Recht und die internationalen Verpflichtungen zwar ZwĂ€nge auferlegen, aber auch Instrumente und Hilfestellungen bieten, um den Weg zurĂŒck in die EuropĂ€ische Union nach Rechtsstaatlichkeitsdefiziten und demokratischen RĂŒckschritten zu erleichtern. Die verschiedenen BeitrĂ€ge untersuchen den verfassungsrechtlichen, rechtlichen und sozialen Rahmen des "Ăbergangs 2.0"
The Topology of Causality
We provide a unified operational framework for the study of causality,
non-locality and contextuality, in a fully device-independent and
theory-independent setting. Our work has its roots in the sheaf-theoretic
framework for contextuality by Abramsky and Brandenburger, which it extends to
include arbitrary causal orders (be they definite, dynamical or indefinite). We
define a notion of causal function for arbitrary spaces of input histories, and
we show that the explicit imposition of causal constraints on joint outputs is
equivalent to the free assignment of local outputs to the tip events of input
histories. We prove factorisation results for causal functions over parallel,
sequential, and conditional sequential compositions of the underlying spaces.
We prove that causality is equivalent to continuity with respect to the
lowerset topology on the underlying spaces, and we show that partial causal
functions defined on open sub-spaces can be bundled into a presheaf. In a
striking departure from the Abramsky-Brandenburger setting, however, we show
that causal functions fail, under certain circumstances, to form a sheaf. We
define empirical models as compatible families in the presheaf of probability
distributions on causal functions, for arbitrary open covers of the underlying
space of input histories. We show the existence of causally-induced
contextuality, a phenomenon arising when the causal constraints themselves
become context-dependent, and we prove a no-go result for non-locality on total
orders, both static and dynamical.Comment: Originally Part 2 of arXiv:2206.08911v2, now extended and published
as a stand-alone paper. Introduction shares some material with Part 1 of the
trilogy, "The Combinatorics of Causality
A Petri net model-based resilience analysis of nuclear power plants under the threat of natural hazards
Due to global climate change, nuclear power plants are increasingly exposed to the threats of extreme natural disasters. In this paper, a resilience engineering approach is applied to tackle all aspects of nuclear safety, spanning from design, operation, and maintenance to accident response and recovery, in the case of high-impact low-probability events. Petri net models are developed to simulate the losses caused by extreme events, the health states of relevant systems, mitigation processes, and the recovery and maintenance processes. The method developed is applied to assess the resilience of a single-unit pressurised heavy water reactor under the threat of three possible external events. Possible loss of coolant accidents and station blackout accidents caused by the events are considered. With the aid of the models developed, both the influence of stochastic deterioration and the impact of external events on the resilience of the reactor can be assessed quantitatively. The simulation results show that the method can comprehensively describe the resilience of nuclear power plants against various disruptive events. It is also found that the stochastic deterioration that does not directly affect the operation of nuclear reactors is critical to the resilience of reactors
Learning Possibilistic Logic Theories
Vi tar opp problemet med Ä lÊre tolkbare maskinlÊringsmodeller fra usikker og manglende informasjon. Vi utvikler fÞrst en ny dyplÊringsarkitektur, RIDDLE: Rule InDuction with Deep LEarning (regelinduksjon med dyp lÊring), basert pÄ egenskapene til mulighetsteori. Med eksperimentelle resultater og sammenligning med FURIA, en eksisterende moderne metode for regelinduksjon, er RIDDLE en lovende regelinduksjonsalgoritme for Ä finne regler fra data. Deretter undersÞker vi lÊringsoppgaven formelt ved Ä identifisere regler med konfidensgrad knyttet til dem i exact learning-modellen. Vi definerer formelt teoretiske rammer og viser forhold som mÄ holde for Ä garantere at en lÊringsalgoritme vil identifisere reglene som holder i et domene. Til slutt utvikler vi en algoritme som lÊrer regler med tilhÞrende konfidensverdier i exact learning-modellen. Vi foreslÄr ogsÄ en teknikk for Ä simulere spÞrringer i exact learning-modellen fra data. Eksperimenter viser oppmuntrende resultater for Ä lÊre et sett med regler som tilnÊrmer reglene som er kodet i data.We address the problem of learning interpretable machine learning models from uncertain and missing information. We first develop a novel deep learning architecture, named RIDDLE (Rule InDuction with Deep LEarning), based on properties of possibility theory. With experimental results and comparison with FURIA, a state of the art method, RIDDLE is a promising rule induction algorithm for finding rules from data. We then formally investigate the learning task of identifying rules with confidence degree associated to them in the exact learning model. We formally define theoretical frameworks and show conditions that must hold to guarantee that a learning algorithm will identify the rules that hold in a domain. Finally, we develop an algorithm that learns rules with associated confidence values in the exact learning model. We also propose a technique to simulate queries in the exact learning model from data. Experiments show encouraging results to learn a set of rules that approximate rules encoded in data.Doktorgradsavhandlin
Serendipity Science
Serendipity is fundamental to science. This quirky and intriguing phenomenon permeates across scientific disciplines, including the medical sciences, psychological sciences, management and organizational sciences, innovation science, philosophy and library and information sciences. Why is it so ubiquitous? Because of what it facilitates and catalyzes: scientific discoveries from velcro to Viagra, innovation of all forms, unexpected encounters of useful information, novel and important ideas, and deep reflection on how we, as individuals, organizations, communities and societies can take leaps forwards by seizing unexpected opportunities and âmaking our own luck.
The great moving countering violent extremism show: An ethnography of CVE in the Canadian context
My dissertation critically examines through ethnographic fieldwork the rise of countering violent extremism [CVE] programs in Canada. CVE is an offshoot of counter-terrorism, with programs first taking hold in the mid-2000s following âhomegrown terrorismâ incidents in Madrid and London. CVE is based on the premise that a âradicalization processâ precedes terrorism. This allows for security and civil society-based interventions in the âpre-crimeâ space to interrupt terrorism before it happens. The most thorough and controversial example of this is the UKâs Prevent strategy, which legally mandates human services professionals to refer individuals showing signs of âradicalizationâ. In Canada, no such duty exists, though its national strategy nonetheless aims to harness âall of societyâ toward preventing violent extremism, enlisting the cooperation of teachers, artists, psychologists, social workers along with actors in the private sector.
My study is not about how individuals turn to âviolent extremismâ or âradicalizationâ but rather about examining that edifices that have created to respond to these perceived problems The implications of CVE as an âall of societyâ endeavour are manifold, particularly as the scope of CVE expands beyond âIslamismâ toward preventing âall typesâ of violent extremism, most recently on right-wing groups and violence against racial, ethnic, and gender minorities. Broadly, my research attempts to conceive of the implications of this expansion. What drives CVEâs growth in the face of sustained criticism over its deleterious impacts on Muslim communities? How do practitioners in CVE align their interests with the cause? What social functions does CVE take on? Moreover, can boundaries even be drawn around what constitutes CVE?
My study draws on interviews with 46 CVE practitioners and participant observation over a three-year period (2018-2020) with CVE entities operating in Canada. My findings indicate how an absence of knowledge over how to conduct CVE propels its encroachment into ever more diverse areas of social life. The paradigm operationalizes âuncertaintyâ to enroll actors with diverse interests and foster partnerships with communities including those (racialized, Indigenous, LGBTQ) that have had fraught relationships with security institutions.
In Chapter 1 - Searching for the CVE space I discuss my immersion in CVE and the type of fieldwork activities conducted. I also attempt to define my research object, outlining how CVE comprises a field of practice, a paradigm, a moral-social imperative, and lastly a space. Chapters 2 and 3 historicize CVEâs contemporary presence and disturb common understandings of its origins. I critique the explanation of CVEâs rise as a necessary and spontaneous reaction to evolving security threats to understand it as an outcome of performative security knowledge, where new security threats are discursively created rather than responded to. Chapters 4 and 5 focus on my fieldwork experience, examining how actors âenrollâ in the CVE cause through the open-ended, speculative quality of its activities. A distinction emerged with Muslim-identifying CVE practitioners, whose motivations to represent their communities in often hostile institutions and reduce the harm of CVE practices were typified by the repeated phrase âif youâre not at the table, youâre on the menuâ. In the conclusion chapter I connect the varying threads of preceding analysis and what they portend for CVEâs effects on societies. This includes examining how CVEâs efforts to redirect political grievances toward âpro-socialâ ends potentially disempowers social justice movements, reinforcing state hegemony and existing power inequities
Fuzzy spectral clustering methods for textual data
Nowadays, the development of advanced information technologies has determined an increase in the production of textual data. This inevitable growth accentuates the need to advance in the identification of new methods and tools able to efficiently analyse such kind of data. Against this background, unsupervised classification techniques can play a key role in this process since most of this data is not classified. Document clustering, which is used for identifying a partition of clusters in a corpus of documents, has proven to perform efficiently in the analyses of textual documents and it has been extensively applied in different fields, from topic modelling to information retrieval tasks. Recently, spectral clustering methods have gained success in the field of text classification. These methods have gained popularity due to their solid theoretical foundations which do not require any specific assumption on the global structure of the data. However, even though they prove to perform well in text classification problems, little has been done in the field of clustering. Moreover, depending on the type of documents analysed, it might be often the case that textual documents do not contain
only information related to a single topic: indeed, there might be an overlap of contents characterizing different knowledge domains. Consequently, documents may contain information that is relevant to different areas of interest to some degree.
The first part of this work critically analyses the main clustering algorithms used for text data, involving also the mathematical representation of documents and the pre-processing phase. Then, three novel fuzzy versions of spectral clustering algorithms for text data are introduced. The first one exploits the use of fuzzy K-medoids instead of K-means. The second one derives directly from the first one but is used in combination with Kernel and Set Similarity (KS2M), which takes into account the Jaccard index. Finally, in the third one, in order to enhance the clustering performance, a new similarity measure Sâ is proposed. This last one exploits the inherent sequential nature of text data by means of a weighted combination between the Spectrum string kernel function and a measure of set similarity.
The second part of the thesis focuses on spectral bi-clustering algorithms for text mining tasks, which represent an interesting and partially unexplored field of research. In particular, two novel versions of fuzzy spectral bi-clustering algorithms are introduced. The two algorithms differ from each other for the approach followed in the identification of the document and the word partitions. Indeed, the first one follows a simultaneous approach while the second one a sequential approach. This difference leads also to a diversification in the choice of the number of clusters. The adequacy of all the proposed fuzzy (bi-)clustering methods is evaluated by experiments performed on both real and benchmark data sets
Syntactic Reasoning with Conditional Probabilities in Deductive Argumentation
Evidence from studies, such as in science or medicine, often corresponds to conditional probability statements. Furthermore, evidence can conflict, in particular when coming from multiple studies. Whilst it is natural to make sense of such evidence using arguments, there is a lack of a systematic formalism for representing and reasoning with conditional probability statements in computational argumentation. We address this shortcoming by providing a formalization of conditional probabilistic argumentation based on probabilistic conditional logic. We provide a semantics and a collection of comprehensible inference rules that give different insights into evidence. We show how arguments constructed from proofs and attacks between them can be analyzed as arguments graphs using dialectical semantics and via the epistemic approach to probabilistic argumentation. Our approach allows for a transparent and systematic way of handling uncertainty that often arises in evidence
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