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Abstract
The presentation proposes to complement an existing development on meaning generation for animals, humans and artificial agents by looking at what could have existed at pre-biotic times and what could be a post-human meaning generation. The core of the approach is based on an existing model for meaning generation: the Meaning Generator System (MGS). The MGS is part of an agent submitted to an internal constraint. The MGS generates a meaning when it receives an information that has a connection with the constraint. The generated meaning is used by the agent to implement an action (physical, biological or mental) aimed at satisfying the constraint. The action can be in or out the agent. The purpose of the presentation is to widen the MGS approach in order to reach a coverage for information, constraint and meaning from a pre-biotic level to a possible post-human one. We present the MGS for animals, humans and artificial agents with the corresponding constraints. We then look at what could have been a constraint at a pre-biotic far from thermodynamic equilibrium level. At the other end of the spectrum we look at a possible post-human status with an evolution of a ‘limit anxiety’ human constraint and also with AAs submitted to animal or human type constraints. Such approach links information science with physics, evolution, anthropology, semiotics and human mind. Continuations are proposed.
Keywords Information  meaning  constraint  evolution  meaning generation  pre-biotic constraint  far from thermodynamic equilibrium  post-human
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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.

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