{"id":1349,"date":"2011-03-23T19:52:57","date_gmt":"2011-03-23T23:52:57","guid":{"rendered":"http:\/\/scientopia.org\/blogs\/goodmath\/?p=1349"},"modified":"2011-03-23T19:52:57","modified_gmt":"2011-03-23T23:52:57","slug":"3-valued-semantics","status":"publish","type":"post","link":"http:\/\/www.goodmath.org\/blog\/2011\/03\/23\/3-valued-semantics\/","title":{"rendered":"3-Valued Semantics"},"content":{"rendered":"

Before we can move from three-valued logic to fuzzy logic, we need to take a look at semantics – both how conventional two-valued logic handle semantics, and how three-valued logics extend the basic semantic structure. This isn’t exactly one of the more exciting topics I’ve ever written about – but it is important, and going through it now will set the groundwork for the interesting stuff – the semantics of a true fuzzy logic.<\/p>\n

What we’ve looked at so far has been propositional 3-valued logics. Propositional logics aren’t particularly interesting. You can’t do or say much with them. What we really care about is predicate logics. But all we need to do is take the three-valued logics we’ve seen, and allow statements to be predicate(object)<\/ic>. <\/p>\n

In a conventional first-order predicate logic, we define the semantics in terms of a model<\/em> or interpretation<\/em> of the logic. (Technically, a logic and an interpretation aren’t quite the same thing, but for our purposes here, we don’t need to get into the difference.) <\/p>\n

An interpretation basically takes a domain<\/em> consisting of a set of objects or values, and does two things:<\/p>\n

    \n
  1. For each atomic symbol in the logic, it assigns an object from the domain. That value is called the interpretation<\/em> of\tthe symbol.<\/li>\n
  2. For each predicate in the logic, it assigns a set, called the extension<\/em> of the predicate. The extension contains the tuples for which the predicate is true.<\/li>\n<\/ol>\n

    For example, we could use logic to talk about Scientopia. The domain<\/em> would be the set of bloggers, and the set of blogs. Then we could have predicates like “Writes”, which takes two parameters – A, and B – and which is true is A is the author of the blog B.<\/p>\n

    Then the extension<\/em> of “Writes” would be a set of pairs: { (MarkCC, Good Math\/Bad Math), (Scicurious, Neurotic Physiology), … }.<\/p>\n

    We can also define the counterextension<\/em>, which is the set of pairs for whiche the predicate is not<\/em> true. So the counterextension of “writes” would contain values like { (MarkCC, Neurotic Physiology), …}.<\/p>\n

    Given a domain of objects, and the extension of each predicate, we know the meaning of statements in the logic. We know what objects we’re reasoning about, and we know the truth or falsehood of every statement. Importantly, we don’t need to know the counterextension of a predicate: if we know the extension, then the counterextension is simple the complement of the extension.<\/p>\n

    In three-valued Lukasiewicz logic, that’s not true, for reasons that should be obvious: if I(A) is the interpretation of the predicate A, then the complement of I(A) is not<\/em> the same thing as I(lnot A). L_3 requires three<\/em> sets for a predicate: the extension, the counterextension, and the fringe<\/em>. The fringe of a predicate P is the set of values x for which P(x) is N.<\/p>\n

    To be more precise, an interpretation I for first order L_3 consists of:<\/p>\n

      \n
    1. A set of values, D, called the domain<\/em> of the logic. This is the set of objects that the logic can be used to reason about.<\/li>\n
    2. For each predicate P of arity n (that is, taking n arguments), three sets ext(P), cext(P), and fringe(P), such that:\n
        \n
      • the values of the members of all three sets are members\t\t\t\tof D^n. <\/li>\n
      • the sets are mutually exclusive – that is, there is no\tvalue that is in more than one of the sets.<\/li>\n
      • the sets are exhaustive<\/em>: ext(P) cup cext(P) cup fringe(P) = D^n.<\/li>\n<\/ul>\n<\/li>\n
      • For each constant symbol a in the logic, an assignment of a to some member of D: I(a) in D<\/li>\n<\/ol>\n

        With the interpretation, we can look at statements in the logic, and determine their truth or falsehood. But when we go through a proof, we’ll often have statements that don’t operate on specific values – they use variables<\/em> inside of them. In order to make a statement have a truth value, all of the variables in that statement have to be bound<\/em> by a quantifier, or assigned<\/em> to a specific value by a variable assignment. So given a statement, we can frequently only talk about its meaning in terms of variable assignments for it.<\/p>\n

        So, for example, consider a simple statement: P(x,y,z). In the interpretation I, P(x, y, z) is satisfied if (I(x), I(y), I(z)) in ext(P); it’s dissatisfied if (I(x), I(y), I(z)) in cext(P). Otherwise, (I(x), I(y), I(z)) must be in fringe(P), and then the statement is undetermined<\/em>.<\/p>\n

        The basic connectives – and, or, not, implies, etc., all have defining rules like the above – they’re obvious and easy to derive given the truth tables for the connectives, so I’m not going to go into detail. But it does get at least a little bit interesting when we get to quantified statements. But to talk about we need to first define a construction called a variant. Given a statement with variable assignment v, which maps all of the variables in the statement to values, an x-variant v of v is a variable assignment where for every variable y except<\/em> x, v. In other words, it’s an assignment where all of the variables except x<\/em> have the same value as in v.<\/p>\n

        Now we can finally get to the interpretation of quantified statements. Given a statement forall x P, P is satisfied<\/em> by a variable assignment v if P is satisfied by every<\/em> x-variant of v; it’s dissatisfied<\/em> if P is dissatisfied by at least<\/em> one x-variant of v. Otherwise, it’s undetermined.<\/p>\n

        Similarly, an existentially quantified statement exists x P is satisfied<\/em> by v if P is satisfied by at least one x-variant of v; it’s dissatisfied if P is dissatisfied by every<\/em> x-variant of v. Otherwise, it’s undetermined.<\/p>\n

        Finally, now, we can get to the most important bit: what it means for a statement to be true or false in L_3. A statement S is T (true) if it is satisfied by every<\/em> variable assignment on I; it’s F (false) if it’s dissatisfied by every variable assignment on I, and it’s N otherwise.<\/p>\n","protected":false},"excerpt":{"rendered":"

        Before we can move from three-valued logic to fuzzy logic, we need to take a look at semantics – both how conventional two-valued logic handle semantics, and how three-valued logics extend the basic semantic structure. This isn’t exactly one of the more exciting topics I’ve ever written about – but it is important, and going […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[87],"tags":[166,193],"class_list":["post-1349","post","type-post","status-publish","format-standard","hentry","category-fuzzy-logic","tag-fuzzy-logic-2","tag-logic-2"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p4lzZS-lL","jetpack_sharing_enabled":true,"jetpack_likes_enabled":true,"_links":{"self":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts\/1349","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/comments?post=1349"}],"version-history":[{"count":0,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts\/1349\/revisions"}],"wp:attachment":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/media?parent=1349"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/categories?post=1349"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/tags?post=1349"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}