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| public:t-720-atai:atai-21:ai_architectures [2021/11/05 13:41] – [Features of NARS] thorisson | public:t-720-atai:atai-21:ai_architectures [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| ====Features of AERA==== | ====Features of AERA==== |
| | Predictable Robustness in Novel Circumstances | \\ Yes | \\ Since AERA's learning is goal driven, its target operational environment are (semi-)novel circumstances. | | | Predictable Robustness in Novel Circumstances | \\ Yes | \\ Since AERA's learning is goal driven, its target operational environment are (semi-)novel circumstances. | |
| | Graceful Degradation | | | | | Graceful Degradation | Yes | Knowledge representation in AERA is based around causal relations, which are essential for mapping out "how the world works". Because AERA's knowledge processing is organized around goals, with increased knowledge AERA will get closer and closer to "perfect operation" (i.e. meeting its top-level drives/goals, for which each instance was created). Furthermore, AERA can do reflection, so it gets better at evaluating its own performance over time, meaning it makes (causal) models of its own failure modes, increasing its chances of graceful degradation. | |
| | \\ Transversal Functions | \\ Yes | //Transversal Handling of Time.// Time is transversal. \\ //Transversal Learning.// Yes. Learning can happen at the smallest level as well as the largest, but generally learning proceeds in small increments. Model-based learning is built in; ampliative (mixed) reasoning is present. \\ //Transversal Analogies.// Yes, but remains to be developed further. \\ //Transversal Self-Inspection.// Yes. AERA can inspect a large part of its internal operations (but not everything). \\ //Transversal Skill Integration.// Yes. This follows naturally from the fact that all models are sharable between anything and everything that AERA learns and does. | | | \\ Transversal Functions | \\ Yes | //Transversal Handling of Time.// Time is transversal. \\ //Transversal Learning.// Yes. Learning can happen at the smallest level as well as the largest, but generally learning proceeds in small increments. Model-based learning is built in; ampliative (mixed) reasoning is present. \\ //Transversal Analogies.// Yes, but remains to be developed further. \\ //Transversal Self-Inspection.// Yes. AERA can inspect a large part of its internal operations (but not everything). \\ //Transversal Skill Integration.// Yes. This follows naturally from the fact that all models are sharable between anything and everything that AERA learns and does. | |
| | \\ Symbolic? | \\ CHECK | One of the main features of AERA is that its knowledge is declarable by being symbol-oriented. AERA can learn language in the same way it learns anything else (i.e. goal-directed, pragmatic). AERA has been implemented to handle 20k models, but so far the most complex demonstration used only approx 1400 models. | | | \\ Symbolic? | \\ CHECK | One of the main features of AERA is that its knowledge is declarable by being symbol-oriented. AERA can learn language in the same way it learns anything else (i.e. goal-directed, pragmatic). AERA has been implemented to handle 20k models, but so far the most complex demonstration used only approx 1400 models. | |
