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| public:t_720_atai:atai-21:causation [2021/09/29 15:15] – created thorisson | public:t_720_atai:atai-21:causation [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| | Why It Is Important | Any environment which cannot be fully known a-priori requires experimentation of some sort, in the form of interaction with the world. This is what we call //experience//. | | | Why It Is Important | Any environment which cannot be fully known a-priori requires experimentation of some sort, in the form of interaction with the world. This is what we call //experience//. | |
| | The Real World | The physical world we live in, often referred to as the "real world", is highly complex, and rarely if ever do we have perfect models of how it behaves when we interact with it, whether it is to experiment with how it works or simply achieve some goal like buying bread. | | | The Real World | The physical world we live in, often referred to as the "real world", is highly complex, and rarely if ever do we have perfect models of how it behaves when we interact with it, whether it is to experiment with how it works or simply achieve some goal like buying bread. | |
| | Limited Time & Resources | An important limitation on any agent's ability to model the real world is its enormous state space, which vastly outdoes any known agent's memory capacity, even for relatively simple environments. Even if the models were sufficiently detailed, pre-computing everything beforehand is prohibited due to memory. On top of that, even if memory would suffice for pre-computing everything and anything necessary to go about our tasks, we would have to retrieve the pre-computed data in time when it's needed - the larger the state space the more demands on retrieval times this puts. | | | \\ Limited Time & Resources | An important limitation on any agent's ability to model the real world is its enormous state space, which vastly outdoes any known agent's memory capacity, even for relatively simple environments. Even if the models were sufficiently detailed, pre-computing everything beforehand is prohibited due to memory. On top of that, even if memory would suffice for pre-computing everything and anything necessary to go about our tasks, we would have to retrieve the pre-computed data in time when it's needed - the larger the state space the more demands on retrieval times this puts. | |
| | Why Experience-Based Learning is Relevant Here | Under LTE (limited time and energy) in a plentiful task-environment it is impossible to know everything all at once, including causal relations. Therefore, most of the time an intelligent agent capable of some reasoning will be working with uncertain assumptions where nothing is certain, only some things are more probable than others. | | | Why Experience-Based Learning is Relevant | \\ Under LTE (limited time and energy) in a plentiful task-environment it is impossible to know everything all at once, including causal relations. Therefore, most of the time an intelligent agent capable of some reasoning will be working with uncertain assumptions where nothing is certain, only some things are more probable than others. | |
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