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  1. added 2020-05-09
    Healthcare and Anomaly Detection: Using Machine Learning to Predict Anomalies in Heart Rate Data.Edin Šabić, David Keeley, Bailey Henderson & Sara Nannemann - forthcoming - AI and Society:1-10.
    The application of machine learning algorithms to healthcare data can enhance patient care while also reducing healthcare worker cognitive load. These algorithms can be used to detect anomalous physiological readings, potentially leading to expedited emergency response or new knowledge about the development of a health condition. However, while there has been much research conducted in assessing the performance of anomaly detection algorithms on well-known public datasets, there is less conceptual comparison across unsupervised and supervised performance on physiological data. Moreover, while (...)
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  2. added 2020-05-08
    Building Machines That Learn and Think About Morality.Christopher Burr & Geoff Keeling - 2018 - In Proceedings of the Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB 2018). Society for the Study of Artificial Intelligence and Simulation of Behaviour.
    Lake et al. propose three criteria which, they argue, will bring artificial intelligence (AI) systems closer to human cognitive abilities. In this paper, we explore the application of these criteria to a particular domain of human cognition: our capacity for moral reasoning. In doing so, we explore a set of considerations relevant to the development of AI moral decision-making. Our main focus is on the relation between dual-process accounts of moral reasoning and model-free/model-based forms of machine learning. We also discuss (...)
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  3. added 2020-04-07
    What Can Artificial Intelligence Do for Scientific Realism?Petr Spelda & Vit Stritecky - forthcoming - Axiomathes:1-20.
    The paper proposes a synthesis between human scientists and artificial representation learning models as a way of augmenting epistemic warrants of realist theories against various anti-realist attempts. Towards this end, the paper fleshes out unconceived alternatives not as a critique of scientific realism but rather a reinforcement, as it rejects the retrospective interpretations of scientific progress, which brought about the problem of alternatives in the first place. By utilising adversarial machine learning, the synthesis explores possibility spaces of available evidence for (...)
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  4. added 2020-04-02
    Semantic Information G Theory and Logical Bayesian Inference for Machine Learning.Chenguang Lu - 2019 - Information 10 (8):261.
    An important problem with machine learning is that when label number n>2, it is very difficult to construct and optimize a group of learning functions, and we wish that optimized learning functions are still useful when prior distribution P(x) (where x is an instance) is changed. To resolve this problem, the semantic information G theory, Logical Bayesian Inference (LBI), and a group of Channel Matching (CM) algorithms together form a systematic solution. MultilabelMultilabel A semantic channel in the G theory consists (...)
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  5. added 2020-02-15
    The Archimedean Trap: Why Traditional Reinforcement Learning Will Probably Not Yield AGI.Samuel Allen Alexander - manuscript
    After generalizing the Archimedean property of real numbers in such a way as to make it adaptable to non-numeric structures, we demonstrate that the real numbers cannot be used to accurately measure non-Archimedean structures. We argue that, since an agent with Artificial General Intelligence (AGI) should have no problem engaging in tasks that inherently involve non-Archimedean rewards, and since traditional reinforcement learning rewards are real numbers, therefore traditional reinforcement learning cannot lead to AGI. We indicate two possible ways traditional reinforcement (...)
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  6. added 2020-02-10
    The Future of Human-Artificial Intelligence Nexus and its Environmental Costs.Petr Spelda & Vit Stritecky - forthcoming - Futures.
    The environmental costs and energy constraints have become emerging issues for the future development of Machine Learning (ML) and Artificial Intelligence (AI). So far, the discussion on environmental impacts of ML/AI lacks a perspective reaching beyond quantitative measurements of the energy-related research costs. Building on the foundations laid down by Schwartz et al., 2019 in the GreenAI initiative, our argument considers two interlinked phenomena, the gratuitous generalisation capability and the future where ML/AI performs the majority of quantifiable inductive inferences. The (...)
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  7. added 2020-01-28
    Predicting Me: The Route to Digital Immortality?Paul Smart - forthcoming - In Robert W. Clowes, Klaus Gärtner & Inês Hipólito (eds.), The Mind-Technology Problem: Investigating Minds, Selves and 21st Century Artifacts. Berlin, Germany:
    An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system that relies on generative models to predict the structure of sensory information. Such a view resonates with a body of work in machine learning that has explored the problem-solving capabilities of hierarchically-organized, multi-layer (i.e., deep) neural networks, many of which acquire and deploy generative models of their training data. The present chapter explores the extent to which the ostensible convergence on a common neurocomputational architecture (...)
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  8. added 2020-01-28
    Situating Machine Intelligence Within the Cognitive Ecology of the Internet.Paul Smart - 2017 - Minds and Machines 27 (2):357-380.
    The Internet is an important focus of attention for the philosophy of mind and cognitive science communities. This is partly because the Internet serves as an important part of the material environment in which a broad array of human cognitive and epistemic activities are situated. The Internet can thus be seen as an important part of the ‘cognitive ecology’ that helps to shape, support and realize aspects of human cognizing. Much of the previous philosophical work in this area has sought (...)
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  9. added 2020-01-28
    Machine Intelligence and the Social Web: How to Get a Cognitive Upgrade.Paul Smart - 2017 - In Vincent Gripon, Olga Chernavskaya, Paul R. Smart & Tiago Thompsen Primo (eds.), 9th International Conference on Advanced Cognitive Technologies and Applications (COGNITIVE'17). Wilmington, DE, USA: pp. 96–103.
    The World Wide Web (Web) provides access to a global space of information assets and computational services. It also, however, serves as a platform for social interaction (e.g., Facebook) and participatory involvement in all manner of online tasks and activities (e.g., Wikipedia). There is a sense, therefore, that the advent of the Social Web has transformed our understanding of the Web. In addition to viewing the Web as a form of information repository, we are now able to view the Web (...)
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  10. added 2020-01-28
    The Social Scaffolding of Machine Intelligence.Paul Smart - 2017 - International Journal on Advances in Intelligent Systems 10 (3&4):261–279.
    The Internet provides access to a global space of information assets and computational services. It also, however, serves as a platform for social interaction (e.g., Facebook) and participatory involvement in all manner of online tasks and activities (e.g., Wikipedia). There is a sense, therefore, that the Internet yields an unprecedented form of access to the human social environment: it provides insight into the dynamics of human behavior (both individual and collective), and it additionally provides access to the digital products of (...)
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  11. added 2019-12-30
    On the Possibility of Emotional Robots.Godwin Darmanin - 2019 - Revista de Filosofia Aurora 31 (54).
    In this article, I examine whether the possibility exists that in the foreseeable future, robot technology will permit the development of emotional robots. As the title suggests, the content is of a technological as well as of a philosophical nature. As a matter of fact, my aim in writing this paper was that of bridging two distinctive fields in a world where humanity has become accustomed to technological innovations while overlooking any consequential complications arising from such inventions. To this end, (...)
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  12. added 2019-12-19
    The Pharmacological Significance of Mechanical Intelligence and Artificial Stupidity.Adrian Mróz - 2019 - Kultura I Historia 36 (2):17-40.
    By drawing on the philosophy of Bernard Stiegler, the phenomena of mechanical (a.k.a. artificial, digital, or electronic) intelligence is explored in terms of its real significance as an ever-repeating threat of the reemergence of stupidity (as cowardice), which can be transformed into knowledge (pharmacological analysis of poisons and remedies) by practices of care, through the outlook of what researchers describe equivocally as “artificial stupidity”, which has been identified as a new direction in the future of computer science and machine problem (...)
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  13. added 2019-11-17
    Changement de Méthode causé par la Numérisation.Jörn Lengsfeld - 2019
    La numérisation va de pair avec un changement fondamental des méthodes qui a le potentiel de changer la pensée, les décisions et les actions des gens. Sur la base de cette thèse, une structure est proposée pour l’analyse de le changement de méthode induit par la numérisation. L’article donne un bref aperçu des forces motrices, des formes et des effets de ce changement méthodologique.
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  14. added 2019-11-17
    Method Change Caused by Digitalization.Jörn Lengsfeld - 2019
    Digitalization goes hand in hand with a fundamental change in methods that has the potential to change people’s thinking, decisions and actions. Departing from this thesis, a structure is proposed for the analysis of the method change induced by digitalization. The article provides a brief outline of the driving forces, the forms and the effects of this method change.
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  15. added 2019-11-12
    Methodenwandel durch Digitalisierung.Jörn Lengsfeld - 2019
    Die Digitalisierung geht mit einem fundamentalen Methodenwandel einher, dem das Potential innewohnt, das Denken, Entscheiden und Handeln der Menschen nachhaltig zu verändern. Ausgehend von dieser These wird eine gliedernde Struktur zur näheren Betrachtung des durch die Digitalisierung induzierten Methodenwandels vorgeschlagen. Der Artikel bietet dazu einen kurzen Abriss über die Triebfedern, die Formen und die Auswirkungen dieses Methodenwandels.
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  16. added 2019-09-25
    Psychopower and Ordinary Madness: Reticulated Dividuals in Cognitive Capitalism.Ekin Erkan - 2019 - Cosmos and History 15 (1):214-241.
    Despite the seemingly neutral vantage of using nature for widely-distributed computational purposes, neither post-biological nor post-humanist teleology simply concludes with the real "end of nature" as entailed in the loss of the specific ontological status embedded in the identifier "natural." As evinced by the ecological crises of the Anthropocene—of which the 2019 Brazil Amazon rainforest fires are only the most recent—our epoch has transfixed the “natural order" and imposed entropic artificial integration, producing living species that become “anoetic,” made to serve (...)
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  17. added 2019-09-09
    Deep Learning: A Philosophical Introduction.Cameron Buckner - 2019 - Philosophy Compass 14 (10).
  18. added 2019-05-30
    The Pragmatic Turn in Explainable Artificial Intelligence (XAI).Andrés Páez - 2019 - Minds and Machines 29 (3):441-459.
    In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will (...)
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  19. added 2019-04-23
    Intelligent Tutoring System for Teaching "Introduction to Computer Science" in Al-Azhar University, Gaza.Ahmad Marouf - 2018 - Dissertation, Al-Azhar University , Gaza
    ITS (Intelligent Tutoring System) is a computer software that supplies direct and adaptive training or response to students without, or with little human teacher interfering. The main target of ITS is smoothing the learning-teaching process using the ultimate technology in computer science. The proposed system will be implemented using the “ITSB” Authoring tool. The book "Introduction To Computer Science" is taught in Al-Azhar University in Gaza as a compulsory subject for students who study at humanities faculties. In this thesis, the (...)
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  20. added 2019-02-28
    Designing AI for Explainability and Verifiability: A Value Sensitive Design Approach to Avoid Artificial Stupidity in Autonomous Vehicles.Steven Umbrello & Roman Yampolskiy - manuscript
    One of the primary, if not most critical, difficulties in the design and implementation of autonomous systems is the black-boxed nature of the decision-making structures and logical pathways of autonomous systems. For this reason, the values of stakeholders become of particular significance given the risks posed by opaque structures of intelligent agents (IAs). This paper proposes the Value Sensitive Design (VSD) approach as a principled framework for incorporating these values in design. The example of autonomous vehicles is used as a (...)
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  21. added 2019-02-11
    Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems.Owen King - 2019 - In Matteo Vincenzo D'Alfonso & Don Berkich (eds.), On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence. Springer Verlag. pp. 265-282.
    Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which they (...)
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  22. added 2019-01-11
    Making AI Meaningful Again.Jobst Landgrebe & Barry Smith - 2019 - Synthese:arXiv:1901.02918v1.
    Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial (...)
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  23. added 2018-12-22
    Plato’s Philosophy of Cognition by Mathematical Modelling.Roman S. Kljujkov & Sergey F. Kljujkov - 2014 - Dialogue and Universalism 24 (3):110-115.
    By the end of his life Plato had rearranged the theory of ideas into his teaching about ideal numbers, but no written records have been left. The Ideal mathematics of Plato is present in all his dialogues. It can be clearly grasped in relation to the effective use of mathematical modelling. Many problems of mathematical modelling were laid in the foundation of the method by cutting the three-level idealism of Plato to the single-level “ideism” of Aristotle. For a long time, (...)
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  24. added 2018-11-12
    A Statistical Learning Approach to a Problem of Induction.Kino Zhao - manuscript
    At its strongest, Hume's problem of induction denies the existence of any well justified assumptionless inductive inference rule. At the weakest, it challenges our ability to articulate and apply good inductive inference rules. This paper examines an analysis that is closer to the latter camp. It reviews one answer to this problem drawn from the VC theorem in statistical learning theory and argues for its inadequacy. In particular, I show that it cannot be computed, in general, whether we are in (...)
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  25. added 2018-09-06
    Content and Misrepresentation in Hierarchical Generative Models.Alex Kiefer & Jakob Hohwy - 2018 - Synthese 195 (6):2387-2415.
    In this paper, we consider how certain longstanding philosophical questions about mental representation may be answered on the assumption that cognitive and perceptual systems implement hierarchical generative models, such as those discussed within the prediction error minimization framework. We build on existing treatments of representation via structural resemblance, such as those in Gładziejewski :559–582, 2016) and Gładziejewski and Miłkowski, to argue for a representationalist interpretation of the PEM framework. We further motivate the proposed approach to content by arguing that it (...)
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  26. added 2018-09-06
    Probabilities on Sentences in an Expressive Logic.Marcus Hutter, John W. Lloyd, Kee Siong Ng & William T. B. Uther - 2013 - Journal of Applied Logic 11 (4):386-420.
    Automated reasoning about uncertain knowledge has many applications. One difficulty when developing such systems is the lack of a completely satisfactory integration of logic and probability. We address this problem directly. Expressive languages like higher-order logic are ideally suited for representing and reasoning about structured knowledge. Uncertain knowledge can be modeled by using graded probabilities rather than binary truth-values. The main technical problem studied in this paper is the following: Given a set of sentences, each having some probability of being (...)
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  27. added 2018-09-02
    Experiences in Mining Educational Data to Analyze Teacher's Performance: A Case Study with High Educational Teachers.Abdelbaset Almasri - 2017 - International Journal of Hybrid Information Technology 10 (12):1-12.
    Educational Data Mining (EDM) is a new paradigm aiming to mine and extract knowledge necessary to optimize the effectiveness of teaching process. With normal educational system work it’s often unlikely to accomplish fine system optimizing due to large amount of data being collected and tangled throughout the system. EDM resolves this problem by its capability to mine and explore these raw data and as a consequence of extracting knowledge. This paper describes several experiments on real educational data wherein the effectiveness (...)
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  28. added 2018-08-30
    Biomedical Ontology Alignment: An Approach Based on Representation Learning.Prodromos Kolyvakis, Alexandros Kalousis, Barry Smith & Dimitris Kiritsis - 2018 - Journal of Biomedical Semantics 9 (21).
    While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic (...)
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  29. added 2018-08-24
    Machine learning, inductive reasoning, and reliability of generalisations.Petr Spelda - 2018 - AI and Society 35 (1):29-37.
    The present paper shows how statistical learning theory and machine learning models can be used to enhance understanding of AI-related epistemological issues regarding inductive reasoning and reliability of generalisations. Towards this aim, the paper proceeds as follows. First, it expounds Price’s dual image of representation in terms of the notions of e-representations and i-representations that constitute subject naturalism. For Price, this is not a strictly anti-representationalist position but rather a dualist one (e- and i-representations). Second, the paper links this debate (...)
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  30. added 2018-08-21
    The Facets of Artificial Intelligence: A Framework to Track the Evolution of AI.Fernando Martínez-Plumed, Bao Sheng Loe, Peter Flach, Sean O. O. HEigeartaigh, Karina Vold & José Hernández-Orallo - 2018 - In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence Evolution of the contours of AI. pp. 5180-5187.
    We present nine facets for the analysis of the past and future evolution of AI. Each facet has also a set of edges that can summarise different trends and contours in AI. With them, we first conduct a quantitative analysis using the information from two decades of AAAI/IJCAI conferences and around 50 years of documents from AI topics, an official database from the AAAI, illustrated by several plots. We then perform a qualitative analysis using the facets and edges, locating AI (...)
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  31. added 2018-03-04
    The Role of Imagination in Social Scientific Discovery: Why Machine Discoverers Will Need Imagination Algorithms.Michael Stuart - 2019 - In Mark Addis, Fernand Gobet & Peter Sozou (eds.), Scientific Discovery in the Social Sciences. Springer Verlag.
    When philosophers discuss the possibility of machines making scientific discoveries, they typically focus on discoveries in physics, biology, chemistry and mathematics. Observing the rapid increase of computer-use in science, however, it becomes natural to ask whether there are any scientific domains out of reach for machine discovery. For example, could machines also make discoveries in qualitative social science? Is there something about humans that makes us uniquely suited to studying humans? Is there something about machines that would bar them from (...)
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  32. added 2018-02-01
    Convergence to the Truth and Nothing but the Truth.Kevin T. Kelly & Clark Glymour - 1989 - Philosophy of Science 56 (2):185-220.
    One construal of convergent realism is that for each clear question, scientific inquiry eventually answers it. In this paper we adapt the techniques of formal learning theory to determine in a precise manner the circumstances under which this ideal is achievable. In particular, we define two criteria of convergence to the truth on the basis of evidence. The first, which we call EA convergence, demands that the theorist converge to the complete truth "all at once". The second, which we call (...)
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  33. added 2018-01-30
    Inductive Logic, Verisimilitude, and Machine Learning.Ilkka Niiniluoto - 2005 - In Petr H’Ajek, Luis Vald’es-Villanueva & Dag Westerståhl (eds.), Logic, methodology and philosophy of science. London: College Publications. pp. 295/314.
    This paper starts by summarizing work that philosophers have done in the fields of inductive logic since 1950s and truth approximation since 1970s. It then proceeds to interpret and critically evaluate the studies on machine learning within artificial intelligence since 1980s. Parallels are drawn between identifiability results within formal learning theory and convergence results within Hintikka’s inductive logic. Another comparison is made between the PAC-learning of concepts and the notion of probable approximate truth.
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  34. added 2018-01-02
    Neural-Symbolic Cognitive Reasoning.Artur D'Avila Garcez, Luis Lamb & Dov Gabbay - 2009 - New York: Springer.
    Humans are often extraordinary at performing practical reasoning. There are cases where the human computer, slow as it is, is faster than any artificial intelligence system. Are we faster because of the way we perceive knowledge as opposed to the way we represent it? -/- The authors address this question by presenting neural network models that integrate the two most fundamental phenomena of cognition: our ability to learn from experience, and our ability to reason from what has been learned. This (...)
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  35. added 2017-04-17
    Political Footprints: Political Discourse Analysis Using Pre-Trained Word Vectors.Christophe Bruchansky - manuscript
    How political opinions are spread on social media has been the subject of many academic researches recently, and rightly so. Social platforms give researchers a unique opportunity to understand how public discourses are perceived, owned and instrumentalized by the general public. This paper is instead focussing on the political discourses themselves, and how a specific machine learning technique - vector space models (VSMs) -, can be used to make systematic and more objective discourse analysis. Political footprints are vector-based representation of (...)
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  36. added 2016-12-02
    The Orbital Space Environment and Space Situational Awareness Domain Ontology – Towards an International Information System for Space Data.Robert J. Rovetto - 2016 Sept - In Proceedings of The Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference.
    The orbital space environment is home to natural and artificial satellites, debris, and space weather phenomena. As the population of orbital objects grows so do the potential hazards to astronauts, space infrastructure and spaceflight capability. Orbital debris, in particular, is a universal concern. This and other hazards can be minimized by improving global space situational awareness (SSA). By sharing more data and increasing observational coverage of the space environment we stand to achieve that goal, thereby making spaceflight safer and expanding (...)
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  37. added 2016-12-02
    Preliminaries of a Space Situational Awareness Ontology.Robert J. Rovetto & T. S. Kelso - 2016 Feb - In Renato Zanetti, Ryan P. Russell, Martin T. Oximek & Angela L. Bowes (eds.), Proceedings of AAS/AIAA Spaceflight Mechanics Meeting, in Advances in the Astronautical Sciences. Univelt Inc.. pp. 4177-4192.
    Space situational awareness (SSA) is vital for international safety and security, and for the future of space travel. The sharing of SSA data and information should improve the state of global SSA for planetary defense and spaceflight safety. I take steps toward a Space Situational Awareness (SSA) Ontology, and outline some central objectives, requirements and desiderata in the ontology development process for this domain. The purpose of this ontological system is to explore the potential for the ontology research topic to (...)
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  38. added 2016-11-28
    Learning Measurement Models for Unobserved Variables.Ricardo Silva, Richard Scheines, Clark Glymour & Peter Spirtes - unknown