Abstract
AbstractBased on our previous researchs about generalized modus ponens (GMP) with linguistic modifiers for If … Then rules, this paper proposes new generalized modus tollens (GMT) inference rules with linguistic modifiers in linguistic many–valued logic framework with using hedge moving rules for inverse approximate reasoning.
Highlights
Information science has brought about an effective tool to help people engaged in computing and reasoning based on natural language
The method works with a final state and is always directed toward the working memory for a goal
In a rule-based system, from a given rule and an observed state of consequent, we conclude something on the state of the antecedent by applying a method of inference which is called inverse approximate reasoning
Summary
Information science has brought about an effective tool to help people engaged in computing and reasoning based on natural language. The question is how to model human’s information processing procedure? C. Ho, Wechler, W. proposed hedge algebraic (HA) structures in order to model the linguistic truth value domain. Based on the hedge algebraic structures, N.C. Ho et al [4] gave a method of linguistic reasoning, and posed further problems to solve. In a rule-based system, from a given rule (antecedentconsequent condition) and an observed state of antecedent, we conclude something by applying a method of inference which is called forward approximate reasoning (using generalized modus ponens for solving forward approximate reasoning). A problem with forward method is that many rules may be applicable for a particular observation (data on antecedent) as the whole process is not directed toward a goal
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