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Computation on Information, Meaning and Representations. An Evolutionary Approach (World Scientific 2011)

In Dodig-Crnkovic, Gordana & Mark Burgin (eds.), Information and Computation. World Scientific. pp. 255-286 (2011)

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  1. Turing Test, Chinese Room Argument, Symbol Grounding Problem. Meanings in Artificial Agents (APA 2013).Christophe Menant - 2013 - American Philosophical Association Newsletter on Philosophy and Computers 13 (1):30-34.
    The Turing Test (TT), the Chinese Room Argument (CRA), and the Symbol Grounding Problem (SGP) are about the question “can machines think?” We propose to look at these approaches to Artificial Intelligence (AI) by showing that they all address the possibility for Artificial Agents (AAs) to generate meaningful information (meanings) as we humans do. The initial question about thinking machines is then reformulated into “can AAs generate meanings like humans do?” We correspondingly present the TT, the CRA and the SGP (...)
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  • Constraint Satisfaction, Agency and Meaning Generation as an Evolutionary Framework for a Constructive Biosemiotic (2019 Update).Christophe Menant - manuscript
    Biosemiotics deal with the study of signs and meanings in living entities. Constructivism considers human knowledge as internally constructed by sense making rather than passively reflecting a pre-existing reality. Consequently, a constructivist perspective on biosemiotics leads to look at an internal active construction of meaning in living entities from basic life to humans. That subject is addressed with an existing tool: the Meaning Generator System (MGS) which is a system submitted to an internal constraint related to the nature of the (...)
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  • Meaning Generation for Animals, Humans and Artificial Agents. An Evolutionary Perspective on the Philosophy of Information. (IS4SI 2017).Christophe Menant - manuscript
    Meanings are present everywhere in our environment and within ourselves. But these meanings do not exist by themselves. They are associated to information and have to be created, to be generated by agents. The Meaning Generator System (MGS) has been developed on a system approach to model meaning generation in agents following an evolutionary perspective. The agents can be natural or artificial. The MGS generates meaningful information (a meaning) when it receives information that has a connection with an internal constraint (...)
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  • Biosemiotics, Aboutness, Meaning and Bio-Intentionality. Proposal for an Evolutionary Approach (Biosemiotics Gatherings 2015).Christophe Menant - manuscript
    The management of meaningful information by biological entities is at the core of biosemiotics [Hoffmeyer 2010]. Intentionality, the ‘aboutness’ of mental states, is a key driver in philosophy of mind. Philosophers have been reluctant to use intentionality for non human animals. Some biologists have been in favor of it. J. Hoffmeyer has been using evolutionary intentionality and Peircean semiotics to discuss a biosemiotic approach to the problem of intentionality [Hoffmeyer 1996, 2012]. Also, recent philosophical studies are bringing new openings on (...)
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  • Meaning Generation for Constraint Satisfaction. An Evolutionary Thread for Biosemiotics (Biosemiotics Gatherings 2016).Christophe Menant - manuscript
    One of the mains challenges of biosemiotics is ‘to attempt to naturalize biological meaning’ [Sharov & all 2015]. That challenge brings to look at a possible evolutionary thread for biosemiotics based on meaning generation for internal constraint satisfaction, starting with a pre-biotic entity emerging from a material universe. Such perspective complements and extends previous works that used a model of meaning generation for internal constraint satisfaction (the Meaning Generator System) [Menant 2003a, b; 2011]. We propose to look at such an (...)
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