rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Logical Structures for Representation of Knowledge and Uncertainty
  name:
    name part: Hisdal, Ellen.
    role:
      role term: creator
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: gw
    date issued: 1998
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (xxiv, 419 pages 81 figs.)
  abstract: To answer questions concerning previously supplied information the book uses a truth table or 'chain set' logic which combines probabilities with truth values (= possibilities of fuzzy set theory). Answers to questions can be 1 (yes); 0 (no); m (a fraction in the case of uncertain information); 0m, m1 or 0m1 (in the case of 'ignorance' or insufficient information). Ignorance (concerning the values of a probability distribution) is differentiated from uncertainty (concerning the occurrence of an outcome). An IF THEN statement is interpreted as specifying a conditional probability value. No predicate calculus is needed in this probability logic which is built on top of a yes-no logic. Quantification sentences are represented as IF THEN sentences with variables. No 'forall' and 'exist' symbols are needed. This simplifies the processing of information. Strange results of first order logic are more reasonable in the chain set logic. E.g., (p->q) AND (p->NOTq), p->NOT p, (p->q)->(p->NOT q), (p->q)->NOT(p->q), are contradictory or inconsistent statements only in the chain set logic. Depending on the context, two different rules for the updating of probabilities are shown to exist. The first rule applies to the updating of IF THEN information by new IF THEN information. The second rule applies to other cases, including modus ponens updating. It corresponds to the truth table of the AND connective in propositional calculus. Many examples of inferences are given throughout the book.
  table of contents: Chain Set and Probability Overview -- BP Chain Sets I, II, and and III -- Inferences or the Answering of Questions -- Inferences with Higher Level Chain Sets -- IF THEN Information -- Various IF THEN Topics -- The M-Notation and Ignorance vs Uncertainty -- Two Types od Updating of Probabilities -- Operations and Ignorance in the M Logic -- Modus Ponens and Existence Updating -- IF THEN Information in the M Logic -- Existence Structures -- Existence Inferences -- Conditional and Joint Existence Information and Inferences -- Attributes and the Alex System vs Chain Sets -- Solutions to Some Exercises.
  note: by Ellen Hisdal.
  note: Electronic resource.
  subject:
    topic: Computer science
  subject:
    topic: Artificial intelligence
  subject:
    topic: Logic, Symbolic and mathematical
  subject:
    topic: Operations research
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer science
  subject:
    topic: Logic, Symbolic and mathematical
  subject:
    topic: Operations research
  classification: Q334-342
  classification: TJ210.2-211.495
  classification: 006.3
  related item:
    title info:
      title: Studies in fuzziness and soft computing ; 14
  identifier: 9783790818871 (electronic bk.)
  identifier: 3790818879 (electronic bk.)
  identifier: 9783790824582
  identifier: 3790824585
  identifier: 3790818879
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: AU@
    record creation date: 130321
    record change date: 20260420215153.4
    record identifier: in00003563967
    language of cataloging:
      language term: eng