# Center for Analysis and Design of Intelligent Agents

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public:t-720-atai:atai-22:ai_architectures

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public:t-720-atai:atai-22:ai_architectures [2022/10/11 14:38]
thorisson [Autonomy & Closure]
public:t-720-atai:atai-22:ai_architectures [2022/10/11 14:38] (current)
thorisson [Autonomy & Closure]
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|  Self-Programming \\ in Autonomy  | The global process that animates computational structurally autonomous systems, i.e. the implementation of both the operational and semantic closures.   | |  Self-Programming \\ in Autonomy  | The global process that animates computational structurally autonomous systems, i.e. the implementation of both the operational and semantic closures.   |
|  System evolution  | A controlled and planned reflective process at a higher level of abstraction than (domain-focused) learning; a global and never-terminating process of architectural analysis and synthesis.  | |  System evolution  | A controlled and planned reflective process at a higher level of abstraction than (domain-focused) learning; a global and never-terminating process of architectural analysis and synthesis.  |
-|  Autonomous Model Acquisition   | \\ The ability to create a model of some target phenomenon //automatically//.   |+|  Autonomous Model Acquisition   | \\ The ability to create a model of some target phenomenon //autonomously// (i.e. without "calling home").   |
|  \\ \\ Challenge  | Unless we (the designers of an intelligent controller) know beforehand which signals from the controller cause desired perturbations in <m>o</m> and can hard-wire these from the get-go, the controller must find these signals. \\ In task-domains where the number of available signals is vastly greater than the controller's resources available to do such search, it may take an unacceptable time for the controller to find good predictive variables to create models with. \\ <m>V_te >> V_mem</m>, where the former is the total number of potentially observable and manipulatable variables in the task-environment and the latter is the number of variables that the agent can hold in its memory at any point in time.   | |  \\ \\ Challenge  | Unless we (the designers of an intelligent controller) know beforehand which signals from the controller cause desired perturbations in <m>o</m> and can hard-wire these from the get-go, the controller must find these signals. \\ In task-domains where the number of available signals is vastly greater than the controller's resources available to do such search, it may take an unacceptable time for the controller to find good predictive variables to create models with. \\ <m>V_te >> V_mem</m>, where the former is the total number of potentially observable and manipulatable variables in the task-environment and the latter is the number of variables that the agent can hold in its memory at any point in time.   |