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Abstract
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 to which the agent is submitted. The generated meaning is to be used by the agent to implement actions aimed at satisfying the constraint. We propose here to highlight some characteristics of the MGS that could be related to items of philosophy of information.
Keywords information  meaning  constraint  evolution  self-consciousness  anxiety management  philosophy of information  symbol grounding problem  truth
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Computation on Information, Meaning and Representations. An Evolutionary Approach (World Scientific 2011).Christophe Menant - 2011 - In Dodig-Crnkovic, Gordana & Mark Burgin (eds.), Information and Computation. World Scientific. pp. 255-286.
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.

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Computation on Information, Meaning and Representations. An Evolutionary Approach (World Scientific 2011).Christophe Menant - 2011 - In Dodig-Crnkovic, Gordana & Mark Burgin (eds.), Information and Computation. World Scientific. pp. 255-286.
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.

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