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Related Experiment Videos

Computing with words.

S H Rubin1

  • 1Dept. of Comput. Sci., Central Michigan Univ., Mount Pleasant, MI.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 7, 2008
PubMed
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Computing with words, a symbolic generalization of fuzzy logic, introduces randomization of knowledge to create procedural knowledge. This approach enhances fuzzy logic through symbolic randomization, offering scalable and knowledge-based learning methods.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Logic

Background:

  • Fuzzy logic provides a framework for reasoning with imprecise information.
  • Existing computational paradigms often lack mechanisms for self-reference and dynamic knowledge adaptation.

Purpose of the Study:

  • To introduce and define Computing with Words (CWW) as a symbolic generalization of fuzzy logic.
  • To explore the concept of knowledge randomization within CWW and its implications for procedural knowledge generation.
  • To demonstrate how CWW can augment fuzzy logic through symbolic randomization.

Main Methods:

  • Defining Computing with Words as a symbolic generalization of fuzzy logic.
  • Introducing two levels of randomization: weak (pattern-matching) and strong (rule-set application).

Related Experiment Videos

  • Proving that effective strong learning must be knowledge-based.

    • Main Results:

    • Computing with words admits self-reference and randomizes declarative knowledge into procedural knowledge.
    • Weak randomization involves domain-general pattern matching; strong randomization applies rule sets to semantics.
    • Strong randomization is a scalable, heuristic process that can optimize search and knowledge base segmentation.

    Conclusions:

    • Computing with words enhances fuzzy logic via symbolic randomization, rather than competing with it.
    • The paradigm supports predicate functions and procedural attachments.
    • An infinite number of domain-specific logics and randomization methods exist within this framework.