| Both sides previous revisionPrevious revisionNext revision | Previous revision |
| public:t-720-atai:atai-22:ai_architectures [2022/10/11 14:12] – [Features of NARS] thorisson | public:t-720-atai:atai-22:ai_architectures [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
|---|
| | \\ Semantic closure | The system's own operations and experience produces/defines the meaning of its constituents. //Meaning// can thus be seen as being defined/given by the operation of the system as a whole: the actions it has taken, is taking, could be taking, and has thought about (simulated) taking, both cognitive actions and external actions in its physical domain. For instance, the **meaning** of the act of punching your best friend are the implications of that act - actual and potential - that this action has/may have, and its impact on your own and others' cognition. | | | \\ Semantic closure | The system's own operations and experience produces/defines the meaning of its constituents. //Meaning// can thus be seen as being defined/given by the operation of the system as a whole: the actions it has taken, is taking, could be taking, and has thought about (simulated) taking, both cognitive actions and external actions in its physical domain. For instance, the **meaning** of the act of punching your best friend are the implications of that act - actual and potential - that this action has/may have, and its impact on your own and others' cognition. | |
| | 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; a global and never-terminating process of architectural 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. | |
| |
| ====Features of NARS==== | ====Features of NARS==== |
| | Predictable Robustness in Novel Circumstances | \\ Yes | NARS is explicitly designed to operate and learn novel things in novel circumstances. It is the only architecture (besides AERA) that is directly based on, and specifically designed to, an **assumption of insufficient knowledge and resources** (AIKR). | | | Predictable Robustness in Novel Circumstances | \\ Yes | NARS is explicitly designed to operate and learn novel things in novel circumstances. It is the only architecture (besides AERA) that is directly based on, and specifically designed to, an **assumption of insufficient knowledge and resources** (AIKR). | |
| | Graceful Degradation | \\ Yes | While the knowledge representation of NARS is not specifically aimed at achieving safe, predictable, or trustworthy operation, NARS can do reflection, so NARS could learn to get better about evaluating its own performance over time, which means it would be increasingly knowledgeable about its failure modes, making it increasingly more likley to fail gracefully. | | | \\ Graceful Degradation | \\ Yes | While the knowledge representation of NARS is not specifically aimed at achieving safe, predictable, or trustworthy operation, NARS can do reflection, so NARS could learn to get better about evaluating its own performance over time, which means it would be increasingly knowledgeable about its failure modes, making it increasingly more likley to fail gracefully. | |
| | \\ Transversal Functions | \\ Yes | //Transversal Handling of Time.// Time is handled in a very general and relative manner, like any other reasoning. \\ //Transversal Learning.// Learning is a central design target of NARS. While knowledge in NARS is not explicitly model-based, its knowledge is symbolic and NARSese statements can be thought of as micro-models; reasoning is a fundamental (some would say the only) principle of its operation. \\ //Transversal Analogies.// Yes \\ //Transversal Self-Inspection.// Yes. Via reasoning. \\ //Transversal Skill Integration.// Yes. Via reasoning. | | | \\ Transversal Functions | \\ Yes | //Transversal Handling of Time.// Time is handled in a very general and relative manner, like any other reasoning. \\ //Transversal Learning.// Learning is a central design target of NARS. While knowledge in NARS is not explicitly model-based, its knowledge is symbolic and NARSese statements can be thought of as micro-models; reasoning is a fundamental (some would say the only) principle of its operation. \\ //Transversal Analogies.// Yes \\ //Transversal Self-Inspection.// Yes. Via reasoning. \\ //Transversal Skill Integration.// Yes. Via reasoning. | |
| | Symbolic? | \\ CHECK | One of the main features of NARS is deep symbol orientation. | | | Symbolic? | CHECK | One of the main features of NARS is deep symbol orientation. | |
| | Models? | No \\ (but yes) | Any good controller of a system is model of that system. The smallest unit in NARS that could be called 'models' are NARSese statements. | | | Models? | No \\ (but yes) | Any good controller of a system is model of that system. The smallest unit in NARS that could be called 'models' are NARSese statements. | |
| |
