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  1. Darwinian 'blind' hypothesis formation revisited.Maria E. Kronfeldner - 2010 - Synthese 175 (2):193--218.
    Over the last four decades arguments for and against the claim that creative hypothesis formation is based on Darwinian ‘blind’ variation have been put forward. This paper offers a new and systematic route through this long-lasting debate. It distinguishes between undirected, random, and unjustified variation, to prevent widespread confusions regarding the meaning of undirected variation. These misunderstandings concern Lamarckism, equiprobability, developmental constraints, and creative hypothesis formation. The paper then introduces and develops the standard critique that creative hypothesis formation is guided (...)
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  • Intelligence without representation.Rodney A. Brooks - 1991 - Artificial Intelligence 47 (1--3):139-159.
    Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity (...)
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  • (1 other version)Computing machinery and intelligence.Alan Turing - 1950 - Mind 59 (October):433-60.
    I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to (...)
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  • Enactive artificial intelligence: Investigating the systemic organization of life and mind.Tom Froese & Tom Ziemke - 2009 - Artificial Intelligence 173 (3-4):466-500.
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  • Rational choice and the structure of the environment.Herbert A. Simon - 1955 - Psychological Review 63 (2):129-138.
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  • A defence of informational structural realism.Luciano Floridi - 2008 - Synthese 161 (2):219-253.
    This is the revised version of an invited keynote lecture delivered at the "1st Australian Computing and Philosophy Conference". The paper is divided into two parts. The first part defends an informational approach to structural realism. It does so in three steps. First, it is shown that, within the debate about structural realism, epistemic and ontic structural realism are reconcilable. It follows that a version of OSR is defensible from a structuralist-friendly position. Second, it is argued that a version of (...)
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  • Shifting the paradigm of philosophy of science: Philosophy of information and a new renaissance. [REVIEW]Gordana Dodig-Crnkovic - 2003 - Minds and Machines 13 (4):521-536.
    Computing is changing the traditional field of Philosophy of Science in a very profound way. First as a methodological tool, computing makes possible ``experimental Philosophy'' which is able to provide practical tests for different philosophical ideas. At the same time the ideal object of investigation of the Philosophy of Science is changing. For a long period of time the ideal science was Physics (e.g., Popper, Carnap, Kuhn, and Chalmers). Now the focus is shifting to the field of Computing/Informatics. There are (...)
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  • How Can Evolution Learn?Richard A. Watson & Eörs Szathmáry - 2016 - Trends in Ecology and Evolution 31 (2):147--157.
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  • Contemporary Natural Philosophy and Philosophies.Gordana Dodig-Crnkovic & Marcin J. Schroeder - 2018 - Philosophies 3 (4):42--0.
    In this Editorial note, Guest Editors introduce the theme of the Special Issue of the journal Philosophies, titled Contemporary Natural Philosophy and Philosophies.
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  • Information, Causation and Computation.John Collier - unknown
    Causation can be understood as a computational process once we understand causation in informational terms. I argue that if we see processes as information channels, then causal processes are most readily interpreted as the transfer of information from one state to another. This directly implies that the later state is a computation from the earlier state, given causal laws, which can also be interpreted computationally. This approach unifies the ideas of causation and computation.
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  • (1 other version)The Unity of Nature.[author unknown] - 1984 - British Journal for the Philosophy of Science 35 (3):274-279.
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