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| public:t-719-nxai:nxai-25:empirical-reasoning [2025/04/27 13:47] – [READINGS] thorisson | public:t-719-nxai:nxai-25:empirical-reasoning [2025/04/27 17:03] (current) – [REQUIRED READINGS] thorisson |
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| ====READINGS==== | |
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| | ====REQUIRED READINGS==== |
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| | * [[https://cis-linux1.temple.edu/~pwang/Publication/learning.pdf|The Logic of Learning]] by P. Wang |
| | * [[https://www.iiim.is/wp/wp-content/uploads/2011/05/wang-agisp-2011.pdf|Behavioral Self-Programming by Reasoning]] by P. Wang |
| | * [[https://philosophynow.org/issues/106/Critical_Reasoning|Critical Reasoning]] by M. Talbot |
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| | Related readings: |
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| | ====Reasoning==== |
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| | | What is Reasoning? | A systematic way of thinking about antecedents, implications, conditionals and inevitables. | |
| | | Empirical Reasoning | A systematic way of thinking about how the world hangs together. | |
| | | How is it done? | Via processes that observe rules. | |
| | | Types of reasoning operations | Deduction: All men are mortal. Socrates is a man. Hence, Socrates is mortal \\ Abduction: How did this come about? (Sherlock Holmes) \\ Induction: "What is the general rule?" \\ Analogy: 'This' is //**like**// 'that' (in 'this' way). | |
| | | How is it used in science? | In empirical science to unearth the "rules of the universe". \\ In mathematics as axioms. \\ In philosophy as a way to construct arguments. \\ In computer science to write code. \\ In AI: To model how intelligent agents learn. | |
| | | Why does it work? | Because the world's behavior is/seems rule-based. | |
| | | But the world is not deterministic! | The world is "pragmatically non-deterministic", meaning that for all practical purposes we must approach it as if it is non-deterministic. \\ Also, we do not know the world's "axioms" -- and cannot ever be sure we will have them. \\ For these reasons, reasoning about the world as if it was axiomatic will not suffice -- we must deal with the unknowns ('noise', 'known unknowns' and 'unknown unknowns') in a systematic manner. This is done through defeasibility methods and non-axiomatic logic. | |
| | | EMPIRICAL REASONING | Defeasible and non-axiomatic reasoning where the data, rules, and results are restricted to the physical world. | |
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| | Subject(s) | Subject of interest - that to be studied, whether people, technology, natural phenomena, or other | | | Subject(s) | Subject of interest - that to be studied, whether people, technology, natural phenomena, or other | |
| | Sample | Typically you can't study all the **individuals** of a particular subject pool (set), so in your experiment you use a **sample** (subset) and hope that the results gathered using this subset generalize to the rest of the set (subject pool). | | | Sample | Typically you can't study all the **individuals** of a particular subject pool (set), so in your experiment you use a **sample** (subset) and hope that the results gathered using this subset generalize to the rest of the set (subject pool). | |
| | Between subjects vs. within subjects design | Between subjects: Two separate groups of subject/phenomena measured \\ Within subjects: Same subjects/phenomena measured twice, on different occasions | | |
| | Quasi-Experimental | When conditions do not permit an **ideal** design to be used (a properly controlled experiment is not possible), there may still be some way to control some of the variables. This is called quasi-experimental design. | | |
| | Dependent variable | The measured variable(s) of the phenomenon which you are studying | | | Dependent variable | The measured variable(s) of the phenomenon which you are studying | |
| | Independent variable | The variable(s) that you manipulate in order to systematically affect (or avoid affecting) the dependent variable(s) | | | Independent variable | The variable(s) that you manipulate in order to systematically affect (or avoid affecting) the dependent variable(s) | |
| | \\ Why It Is Important \\ in AI | Probability enters into our knowledge of anything for which the knowledge is //**incomplete**//. \\ As in, //everything that humans do every day in every real-world environment//. \\ With incomplete knowledge it is in principle //impossible to know what may happen//. However, if we have very good models for some //limited// (small, simple) phenomenon, we can expect our prediction of what may happen to be pretty good, or at least //**practically useful**//. This is especially true for knowledge acquired through the scientific method, in which empirical evidence and human reason is systematically brought to bear on the validity of the models. | | | \\ Why It Is Important \\ in AI | Probability enters into our knowledge of anything for which the knowledge is //**incomplete**//. \\ As in, //everything that humans do every day in every real-world environment//. \\ With incomplete knowledge it is in principle //impossible to know what may happen//. However, if we have very good models for some //limited// (small, simple) phenomenon, we can expect our prediction of what may happen to be pretty good, or at least //**practically useful**//. This is especially true for knowledge acquired through the scientific method, in which empirical evidence and human reason is systematically brought to bear on the validity of the models. | |
| | How To Compute Probabilities | Most common method is Bayesian networks, which encode the concept of probability in which probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief [[https://en.wikipedia.org/wiki/Bayesian_probability|REF]]. Which makes it useful for representing an (intelligent) agent's knowledge of some environment, task or phenomenon. | | | How To Compute Probabilities | Most common method is Bayesian networks, which encode the concept of probability in which probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief [[https://en.wikipedia.org/wiki/Bayesian_probability|REF]]. Which makes it useful for representing an (intelligent) agent's knowledge of some environment, task or phenomenon. | |
| | \\ How It Works | \\ P(a#b) = {P(b#a) P(a)} / {P(b)} \\ where # means 'given'. | | | How It Works | P(a#b) = {P(b#a) P(a)} / {P(b)} \\ where # means 'given'. | |
| | Judea Pearl | Most Fervent Advocate (and self-proclaimed inventor) of Bayesian Networks in AI [[http://ftp.cs.ucla.edu/pub/stat_ser/R246.pdf|REF]]. | | | Judea Pearl | Most Fervent Advocate (and self-proclaimed inventor) of Bayesian Networks in AI [[http://ftp.cs.ucla.edu/pub/stat_ser/R246.pdf|REF]]. | |
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| ====Causation & AI==== | ====Causation & AI==== |
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| | Correlation Supports Prediction | Correlation is sufficient for simple prediction (if <m>A</m> and <m>B</m> correlate highly, then it does not matter if we see an <m>A</m> //OR// a <m>B</m>, we can predict that the other is likely on the scene). | | | Correlation Supports Prediction | Correlation is sufficient for simple prediction (if A and B correlate highly, then it does not matter if we see an A //OR// a B, we can predict that the other is likely on the scene). | |
| | \\ Knowledge of Causation Supports Action | We may know that <m>A</m> and <m>B</m> correlate, but if we don't know whether <m>B</m> is a result of <m>A</m> or vice versa, and we want <m>B</m> to disappear, we don't know whether it will suffice to modify <m>A</m>. \\ //Example: The position of the light switch and the state of the light bulb correlate. Only by knowing that the light switch controls the bulb can we go directly to the switch if we want the light to turn on. // | | | \\ Knowledge of Causation Supports Action | We may know that A and B correlate, but if we don't know whether B is a result of A or vice versa, and we want B to disappear, we don't know whether it will suffice to modify A. \\ //Example: The position of the light switch and the state of the light bulb correlate. Only by knowing that the light switch controls the bulb can we go directly to the switch if we want the light to turn on. // | |
| | **Causal Models** \\ Are Necessary To Guide Action | While correlation gives us indication of causation, the direction of the "causal arrow" is critically necessary for guiding action. \\ Luckily, knowing which way the arrows point in any large set of correlated variables is usually not too hard to find out, by empirical experimentation. | | | **Causal Models** \\ Are Necessary To Guide Action | While correlation gives us indication of causation, the direction of the "causal arrow" is critically necessary for guiding action. \\ Luckily, knowing which way the arrows point in any large set of correlated variables is usually not too hard to find out, by empirical experimentation. | |
| | Judea Pearl | Most Fervent Advocate of causality in AI, and the inventor of the Do Calculus. \\ C.f. [[https://ftp.cs.ucla.edu/pub/stat_ser/r284-reprint.pdf|BAYESIANISM AND CAUSALITY, OR, WHY I AM ONLY A HALF-BAYESIAN]]. | | | Judea Pearl | Most Fervent Advocate of causality in AI, and the inventor of the Do Calculus. \\ C.f. [[https://ftp.cs.ucla.edu/pub/stat_ser/r284-reprint.pdf|BAYESIANISM AND CAUSALITY, OR, WHY I AM ONLY A HALF-BAYESIAN]]. | |
