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  1.  22
    Nietzsche y la vida interpretante.Luis Eduardo Gama Barbosa - 2014 - Praxis Filosófica 39:171-196.
    Contra un presunto naturalismo de la filosofía de Nietzsche, este artículo reconstruye la noción nietzscheana de vida al hilo del tema de la interpretación. Se pretende mostrar que para este autor el problema de la vida excede todo marco estrictamente biologicista y se orienta en cambio hacia una dimensión ontológica, en tanto la interpretación representa la actividad configuradora de sentido, mediante la cual todo ser vivo genera y sostiene su realidad. El análisis de este proceso lleva a identificar varios niveles (...)
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  2.  33
    A Hilbert-Style Axiomatisation for Equational Hybrid Logic.Luís S. Barbosa, Manuel A. Martins & Marta Carreteiro - 2014 - Journal of Logic, Language and Information 23 (1):31-52.
    This paper introduces an axiomatisation for equational hybrid logic based on previous axiomatizations and natural deduction systems for propositional and first-order hybrid logic. Its soundness and completeness is discussed. This work is part of a broader research project on the development a general proof calculus for hybrid logics.
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  3.  83
    A note on equivalent comparisons of information channels.Luís Fernando Brands Barbosa & Gil Riella - 2015 - Theory and Decision 78 (1):33-44.
    Nakata (Theory Decis 71:559–574, 2011) presents a model of acquisition of information where the agent does not know what pieces of information she is missing. In this note, we point out some technical problems in a few of Nakata’s results and show how to correct them.
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  4.  15
    The challenge of ontology in interpretationalism.Luis Eduardo Gama Barbosa - 2018 - In Astrid Wagner & Ulrich Dirks (eds.), Abel Im Dialog: Perspektiven der Zeichen- Und Interpretationsphilosophie. De Gruyter. pp. 1345-1364.
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  5.  19
    Quantum Bayesian Decision-Making.Michael de Oliveira & Luis Soares Barbosa - 2021 - Foundations of Science 28 (1):21-41.
    As a compact representation of joint probability distributions over a dependence graph of random variables, and a tool for modelling and reasoning in the presence of uncertainty, Bayesian networks are of great importance for artificial intelligence to combine domain knowledge, capture causal relationships, or learn from incomplete datasets. Known as a NP-hard problem in a classical setting, Bayesian inference pops up as a class of algorithms worth to explore in a quantum framework. This paper explores such a research direction and (...)
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