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| public:t-720-atai:atai-19:aera [2019/10/20 18:45] – [Model Generation & Evaluation] thorisson | public:t-720-atai:atai-19:aera [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| | {{public:t-720-atai:three-models-1.png?400}} | | | {{public:t-720-atai:three-models-1.png?400}} | |
| | Based on prior observations, of the variables and their temporal execution in some context, the controller's model generation process <m>P_M</m> may have captured their causal relationship in three alternative models, <m>M_1, M_2, M_3</m>, each slightly but measurably different from the others. Each can be considered a //hypothesis of the actual relationship between the referenced variables//, when in the context provided by <m>V_5, V_6</m>. \\ As an example, we could have a tennis ball's direction <m>M_1</m>, speed <m> M_2</m>, and shape <m>M_3</m> that changes when it hits a wall <m>V_5</m>, according to its relative angle <m>V_6</m> to the wall. | | | Based on prior observations, of the variables and their temporal execution in some context, the controller's model generation process <m>P_M</m> may have captured their causal relationship in three alternative models, <m>M_1, M_2, M_3</m>, each slightly but measurably different from the others. Each can be considered a //hypothesis of the actual relationship between the referenced variables//, when in the context provided by <m>V_5, V_6</m>. \\ As an example, we could have a tennis ball's direction <m>V_1</m>, speed <m>V_2</m>, and shape <m>V_3</m> that changes when it hits a wall <m>V_5</m>, according to its relative angle <m>V_6</m> to the wall. | |
| | {{public:t-720-atai:agent-with-models-1.png?300}} | | | {{public:t-720-atai:agent-with-models-1.png?300}} | |
| | The agent's model generation mechanisms allow it to produce models of events it sees. Here it creates models (a) <m>M_1</m> and (b) <m>M_2</m>. The usefulness / utility of these models can be tested by performing an operation on the world (c ) as prescribed by the models. (Ideally, when one wants to find on which one is best, the most efficient method is an (energy-preserving) intervention that can only leave one as the winner.) | | | The agent's model generation mechanisms allow it to produce models of events it sees. Here it creates models (a) <m>M_1</m> and (b) <m>M_2</m>. The usefulness of these models for particular situations and goals can be tested by performing an operation on the world (c ) as prescribed by the models, through backward chaining (abduction). \\ Ideally, when one wants to find on which model is best for a particular situation (goals+environment+state), the most efficient method is an (energy-preserving) intervention that can only leave one as the winner. | |
| | {{public:t-720-atai:model-m2-prime-1.png?150}} | | | {{public:t-720-atai:model-m2-prime-1.png?150}} | |
| | The result of feedback (reinforcement) may result in the deletion, rewriting, or some other modification of the original model selected for prediction. Here the feedback has resulted in a modified model <m>M{prime}_2</m>. | | | The feedback (reinforcement) resulting from direct or indirect tests of a model may result in its deletion, rewriting, or some other modification. Here the feedback has resulted in a modified model <m>M{prime}_2</m>. | |
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