Answers.Body: <p>This was a somewhat hotly debated question in the 1980s. The debate was more-or-less ended with papers like Cheeseman's <em><a href="https://www.ijcai.org/Proceedings/85-2/Papers/064.pdf" rel="noreferrer">In Defense of Probability</a></em>.</p>
  
  <p>The short answer is that Fuzzy Logic does not <em>just</em> assign a continuous value to sentences, what it does is assign <em>degrees of membership</em> in different <em>fuzzy sets</em>. These degrees of membership range between 0 and 1.</p>
  
  <p>In contrast, probability says: Among the set of values this variable could take on, what <em>fraction</em> of them are in a certain set? This fraction also ranges between 0 and 1.</p>
  
  <p>This might seem like a semantic distinction, but it has deep implications if you try to use these systems to reason about uncertainty.</p>
  
  <p>For example, consider the question "Will it rain tomorrow?". A probabilistic approach would try to determine the fraction of days like tomorrow that have had rain. A fuzzy logic approach will try to determine whether tomorrow is <em>like</em> a rainy day. The distinction becomes obvious if we then ask whether it will <em>not</em> rain tomorrow. The probabilistic approach will try to find the fraction of days like tomorrow where it did not rain. The chance of it raining or not raining will sum to 1. The fuzzy logic approach will try to determine whether tomorrow is <em>like</em> a non-rainy day. Note that tomorrow may be both like a stereotypical rainy day <em>and</em> not like one. There is no firm requirement that these sets be disjoint. This reflects Cheeseman's critique of Fuzzy Sets: they implicitly reject the additive axiom of probability theory, which (as denoted by the name <em>axiom</em>) is something that is fairly unreasonable to reject.</p>
  
  <p>While there are various approaches to make memberships in fuzzy sets more probabilistic in nature, that's the root distinction. </p>
  
  <p>I think the dominant modern view is that fuzzy sets are a great tool when you need to reason about membership in a fuzzy concept. Whether <a href="https://rads.stackoverflow.com/amzn/click/com/B00007J5U7" rel="noreferrer" rel="nofollow noreferrer">rice is "done" or not</a> isn't a question of something being true or false. It's a degree of membership in the set "done". On the other hand, whether the object in front of a self-driving car is a person or not is either true or false. It is not necessarily a good idea to reason about this in terms of whether the object is partially in the person set or plastic bag set. </p>
  
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Answers.Text: Do all fuzzy operators reject the additive axiom? Or do the ones based on the product respect additivity? Related question:  https://ai.stackexchange.com/questions/38119/does-this-property-in-product-fuzzy-logic-have-a-name-and-any-consequences
  Maybe the answer to the linked question is "additivity"?
Answers.UserDisplayName: Jaume Oliver Lafont
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Body: <p>I understand fuzzy logic is a variant of formal logic where, instead of just 0 or 1, a given sentence may have a truth value in the [0..1] interval. Also, I understand that logical probability (objective bayesian) understands probability as an extension of logic, where uncertainity is taken into account. To me they sound rather similar (they both extend formal logic by modelling truth as a continuos interval between 0 and 1).</p>
  
  <p>My question is, <strong>what is the relationship between these two concepts?. What is the difference, and what are the differences in AI approaches based upon these two formal systems?</strong></p>
  
Tags: <fuzzy-logic><bayesian-probability>
Title: What is the relationship between fuzzy logic and objective bayesian probability?
UserDisplayName: olinarr
ThreadText: # What is the relationship between fuzzy logic and objective bayesian probability?
  
  (Asked by: username_0 on 2019-10-26)
  
  I understand fuzzy logic is a variant of formal logic where, instead of just 0 or 1, a given sentence may have a truth value in the [0..1] interval. Also, I understand that logical probability (objective bayesian) understands probability as an extension of logic, where uncertainity is taken into account. To me they sound rather similar (they both extend formal logic by modelling truth as a continuos interval between 0 and 1).
  
  My question is, **what is the relationship between these two concepts?. What is the difference, and what are the differences in AI approaches based upon these two formal systems?**
  
  **Tags:** fuzzy-logic, bayesian-probability
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-10-26. Score: 7 | Accepted answer
  
  This was a somewhat hotly debated question in the 1980s. The debate was more-or-less ended with papers like Cheeseman's *[In Defense of Probability](https://www.ijcai.org/Proceedings/85-2/Papers/064.pdf)*.
  
  The short answer is that Fuzzy Logic does not *just* assign a continuous value to sentences, what it does is assign *degrees of membership* in different *fuzzy sets*. These degrees of membership range between 0 and 1.
  
  In contrast, probability says: Among the set of values this variable could take on, what *fraction* of them are in a certain set? This fraction also ranges between 0 and 1.
  
  This might seem like a semantic distinction, but it has deep implications if you try to use these systems to reason about uncertainty.
  
  For example, consider the question "Will it rain tomorrow?". A probabilistic approach would try to determine the fraction of days like tomorrow that have had rain. A fuzzy logic approach will try to determine whether tomorrow is *like* a rainy day. The distinction becomes obvious if we then ask whether it will *not* rain tomorrow. The probabilistic approach will try to find the fraction of days like tomorrow where it did not rain. The chance of it raining or not raining will sum to 1. The fuzzy logic approach will try to determine whether tomorrow is *like* a non-rainy day. Note that tomorrow may be both like a stereotypical rainy day *and* not like one. There is no firm requirement that these sets be disjoint. This reflects Cheeseman's critique of Fuzzy Sets: they implicitly reject the additive axiom of probability theory, which (as denoted by the name *axiom*) is something that is fairly unreasonable to reject.
  
  While there are various approaches to make memberships in fuzzy sets more probabilistic in nature, that's the root distinction.
  
  I think the dominant modern view is that fuzzy sets are a great tool when you need to reason about membership in a fuzzy concept. Whether [rice is "done" or not](https://rads.stackoverflow.com/amzn/click/com/B00007J5U7) isn't a question of something being true or false. It's a degree of membership in the set "done". On the other hand, whether the object in front of a self-driving car is a person or not is either true or false. It is not necessarily a good idea to reason about this in terms of whether the object is partially in the person set or plastic bag set.
  
  > **Comments:**
  > **username_2**: Do all fuzzy operators reject the additive axiom? Or do the ones based on the product respect additivity? Related question:  https://ai.stackexchange.com/questions/38119/does-this-property-in-product-fuzzy-logic-have-a-name-and-any-consequences Maybe the answer to the linked question is "additivity"?  
  
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