  As the two basic fuzzy inference models, fuzzy modus ponens (FMP) and fuzzy
modus tollens (FMT) have the important application in artificial intelligence.
In order to solve FMP and FMT problems, Zadeh proposed a compositional rule of
inference (CRI) method. This paper aims mainly to investigate the validity of
A-compositional rule of inference (ACRI) method, as a generalized CRI method
based on aggregation functions, from a logical view and an interpolative view,
respectively. Specifically, the modus ponens (MP) and modus tollens (MT)
properties of ACRI method are discussed in detail. It is shown that the
aggregation functions to implement FMP and FMT problems provide more generality
than the t-norms, uninorms and overlap functions as well-known the laws of
T-conditionality, U-conditionality and O-conditionality, respectively.
Moreover, two examples are also given to illustrate our theoretical results.
Especially, Example 6.2 shows that the output B' in FMP(FMT) problem is close
to B(DC) with our proposed inference method when the fuzzy input and the
antecedent of fuzzy rule are near (the fuzzy input near with the negation of
the seccedent in fuzzy rule).
