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| public:t-713-mers:mers-24:empirical-reasoning-2 [2024/09/24 13:11] – [Uncertainty in Physical Worlds] thorisson | public:t-713-mers:mers-24:empirical-reasoning-2 [2024/11/05 11:44] (current) – thorisson |
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| ======Empirical Reasoning (2)====== | ======Empirical Reasoning (II)====== |
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| ====Uncertainty in Physical Worlds==== | =====Uncertainty in Physical Worlds===== |
| | What it is | In a dynamic world with a large number of elements and processes, presenting infinite combinatorics, knowing everything is impossible and thus predicting everything is also impossible. || | | What it is | In a dynamic world with a large number of elements and processes, presenting infinite combinatorics, knowing everything is impossible and thus predicting everything is also impossible. || |
| | Stems From | ------ Unknown Things / Phenomena ------ || | | Stems From | ------ Unknown Things / Phenomena ------ || |
| | | Variable Values | //E.g. we know it will eventually rain, but not exactly when.// | | | | Variable Values | //E.g. we know it will eventually rain, but not exactly when.// | |
| | | Variables | //E.g. a gust of wind that hits us as we come around a skyscraper's corner.// \\ This method of defining "variable" equates 'variable' with 'percept'; percepts can be thought of as units of thought, or "chunks", that are treated (for all practical purposes, in any particular situation) as a single thing or entity. | | | | Variables | //E.g. a gust of wind that hits us as we come around a skyscraper's corner.// \\ This method of defining "variable" equates 'variable' with 'percept'; percepts can be thought of as units of thought, or "chunks", that are treated (for all practical purposes, in any particular situation) as a single thing or entity when thinking and doing reasoning. | |
| | | Goals of Others | //E.g. when we meet someone in the street and move to our right, but they also move in that direction (to their left), at which point we move to our left, but they move to their right, etc., for a sequence of synchronized stalemate.// | | | | Goals of Others | //E.g. when we meet someone in the street and move to our right, but they also move in that direction (to their left), at which point we move to our left, but they move to their right, etc., for a sequence of synchronized stalemate.// | |
| | | Imprecision in Measurements | //E.g. the position of your car on the road relative to other cars and the boundaries of the road.// | | | | Imprecision in Measurements | //E.g. the position of your car on the road relative to other cars and the boundaries of the road.// | |
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| ====Signal & Noise==== | =====Signal & Noise===== |
| | Modeling the World | A fundamental method in engineering is to model dynamic systems as part "signal" and part "noise" -- the former is what we have a good handle on, so we can turn it into a 'signal', and the latter is what we (currently) are unable to model, making it random (hence 'noise'). | | | Modeling the World | A fundamental method in engineering is to model dynamic systems as part "signal" and part "noise" -- the former is what we have a good handle on, so we can turn it into a 'signal', and the latter is what we (currently) are unable to model, making it random (hence 'noise'). | |
| | Infinite Worlds as 'signal' & 'noise' | In artificial general intelligence it can be useful to think of knowledge in the same way: Anything for which there exists good models (read: useful knowledge) we look at as 'signal' and anything else (which looks more or less random to us) is 'noise'. | | | Infinite Worlds as 'signal' & 'noise' | In artificial general intelligence it can be useful to think of knowledge in the same way: Anything for which there exists good models (read: useful knowledge) we look at as 'signal' and anything else (which looks more or less random to us) is 'noise'. | |
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| ==== Methods For Dealing With Uncertainty ==== | ===== Methods For Dealing With Uncertainty ===== |
| | Model Creation | Model creation from experience is the key method for dealing with uncertainty. \\ Models, combined with reasoning, can produce generalized information structures that can be used for several purposes, including prediction, explanation, planning, goal selection, classification, and many other cognitive activities. | | | Model Creation | Model creation from experience is the key method for dealing with uncertainty. \\ Models, combined with reasoning, can produce generalized information structures that can be used for several purposes, including prediction, explanation, planning, goal selection, classification, and many other cognitive activities. | |
| | \\ Reasoning | Aka "rule creation" aka "generalization". \\ 'Induction' is another term for generalization, but it's not only reasoning through induction that matters - deduction, abduction and analogy (all defeasible, non-axiomatic) that uncertainty handling relies on. | | | \\ Reasoning | Aka "rule creation" aka "generalization". \\ 'Induction' is another term for generalization, but it's not only reasoning through induction that matters - deduction, abduction and analogy (all defeasible, non-axiomatic) that uncertainty handling relies on. | |
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| ==== Backward & Forward Chaining in Production Systems ==== | ===== Backward & Forward Chaining in Production Systems ===== |
| | Matching | Rules are matched to conditions by **matching** -- if a rule matches is found to match a pattern in a particular dataset, the rule **fires**. When a rule fires it means that its statements will be executed. | | | Matching | Rules are matched to conditions by **matching** -- if a rule matches is found to match a pattern in a particular dataset, the rule **fires**. When a rule fires it means that its statements will be executed. | |
| | \\ Production System | Sometimes 'production system' is used as a synonym to 'reasoning system'. However, while both are rule-based, reasoning systems often come with requirements and limitations that production systems are (typically) not subject to. 'Production systems' is a larger set of systems than 'reasoning systems', but the strictest sense of 'reasoning system' (e.g. first-order logic) is not part of the set of 'production systems'. | | | \\ Production System | Sometimes 'production system' is used as a synonym to 'reasoning system'. However, while both are rule-based, reasoning systems often come with requirements and limitations that production systems are (typically) not subject to. 'Production systems' is a larger set of systems than 'reasoning systems', but the strictest sense of 'reasoning system' (e.g. first-order logic) is not part of the set of 'production systems'. | |
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| ==== Guided Experimentation for New Knowledge Generation ==== | ===== Guided Experimentation for New Knowledge Generation ===== |
| | \\ Experimenting on the World | Knowledge-guided experimentation is the process of using one's current knowledge to create more knowledge. When learning about the world, random exploration is by definition the slowest and most ineffective knowledge creation method; in complex worlds it may even be completely useless due to the world's combinatorics. (If the ratio of complexity to lack of knowledge guidance is too high, no learning can take place.) \\ Strategic experimentation for knowledge generation involves conceiving actions that minimize energy and time while optimizing the exclusion of families of hypotheses about how the world works. | | | \\ Experimenting on the World | Knowledge-guided experimentation is the process of using one's current knowledge to create more knowledge. When learning about the world, random exploration is by definition the slowest and most ineffective knowledge creation method; in complex worlds it may even be completely useless due to the world's combinatorics. (If the ratio of complexity to lack of knowledge guidance is too high, no learning can take place.) \\ Strategic experimentation for knowledge generation involves conceiving actions that minimize energy and time while optimizing the exclusion of families of hypotheses about how the world works. | |
| | Inspecting One's Own Knowledge | Inspection of knowledge happens via //**reflection**// -- the ability to apply learning mechanisms to the processes and content of one's own mind. Reflection enables a learner to set itself a goal, then inspect that goal, producing arguments for and against that goal's features (usefulness, justification, time- and energy-dependence, and so on...). In other words, reflection gives a mind a capacity for **//meta-knowledge//**. | | | Inspecting One's Own Knowledge | Inspection of knowledge happens via //**reflection**// -- the ability to apply learning mechanisms to the processes and content of one's own mind. Reflection enables a learner to set itself a goal, then inspect that goal, producing arguments for and against that goal's features (usefulness, justification, time- and energy-dependence, and so on...). In other words, reflection gives a mind a capacity for **//meta-knowledge//**. | |
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| ====Cumulative Learning==== | =====Cumulative Learning===== |
| | What it Is | Learning where several separate learning goals are unified into a single holistic learning system: Multitask learning, lifelong learning, transfer learning and few-shot learning. \\ (Research on learning typically separates these and works on a single aspect at a time.) || | | What it Is | Learning where several separate learning goals are unified into a single holistic learning system: Multitask learning, lifelong learning, transfer learning and few-shot learning. \\ (Research on learning typically separates these and works on a single aspect at a time.) || |
| || Unifying All of These || | || Unifying All of These || |
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| ==== Self-Explaining Systems ==== | ===== Self-Explaining Systems ===== |
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| | What It Is | The ability of a controller to explain, after the fact or before, why it did something or intends to do it. | | | What It Is | The ability of a controller to explain, after the fact or before, why it did something or intends to do it. | |
