Parameterized Fuzzy Decision Implication

Intelligent decision making is an important part of artificial intelligence. In formal concept analysis, decision is represented by decision implication in decision context, while fuzzy decision implication is based on fuzzy decision context, whose premise and conclusion are condition attributes and decision attributes respectively. Fuzzy decision implication exhibits a wider application significance, because it can avoid the fuzzy attribute implications that occur between condition attributes and between decision attributes. Deterministic, unadjustable knowledge acquisition is poorly adaptive to practical applications. Therefore, we need to expand unadjustable knowledge discovery methods to parameterized adjustable knowledge discovery methods. As two kinds of parameterized strategies, hedges and thresholds play an important role in the research of formal concept analysis with fuzzy attributes. Existing fuzzy decision implication models only take into account the hedge operator, which is poor in tunability, and the parameterized strategy that considers the threshold is less studied. Thus, in our paper, complete residual lattice is used as the reference frame, and the two parameterized strategies of hedge and threshold are introduced into fuzzy decision implication. We study the semantic aspect and prove some basic properties. Then we put forward three inference rules for parameterized fuzzy decision implication based knowledge reasoning and show their rationality and completeness. According to the results obtained, in real life, users may choose appropriate hedge and threshold to acquire knowledge, thus enhancing the tunability and application value of fuzzy decision implication.