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Abstract
Information and meanings are present everywhere around us and within ourselves. Specific studies have been implemented in order to link information and meaning (Linguistic, Semiotic, Biosemiotic, Psychology, Psychiatry, Biology, Neurology, Cognition, Artificial Intelligence... ). No general coverage is available for the notion of meaning. We propose to complement this lack by a system approach for meaning genaration. A Meaning Generator System based on constraint satisfaction is presented. It can be used for animals, humans and artificial agents, and makes available definitions for agency and autonomy. Usage of the MGS with the Turing Test shows why today computers cannot think like humans do. The MGS introduces evolutionary scenarios for cognition, intentionality and self-consciousness where a specific human anxiety is highlighted. Anxiety limitation is then a key human constraint tightly related to an evolutionary nature of self-consciousness. That new perspective positions mostly unconscious anxiety management processes as driving human mental states and conditioning our mental health, much more than assumed so far. Continuations are proposed.
Keywords Information  meaning  constraint  human mind  representation  evolution  self-consciousness  artificial agent  Turing test  artificial intelligence  mental state
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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.
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.

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