As an extension of probability theory, evidence theory is able to better handle unknown and
imprecise information. Owing to its advantages, evidence theory has more flexibility and …

In artificial intelligence systems, a question on how to express the uncertainty in knowledge
remains an open issue. The negation scheme provides a new perspective to solve this …

Complex evidence theory (CET) is an effective method for uncertainty reasoning in
knowledge-based systems with good interpretability that has recently attracted much …

Uncertainty is inevitable in the decision-making process of real applications. Quantum
mechanics has become an interesting and popular topic in predicting and explaining human …

Given a probability distribution, its corresponding information volume is Shannon entropy.
However, how to determine the information volume of a given mass function is still an open …

Understanding the uncertainty involved in a mass function is a central issue in Dempster–
Shafer evidence theory for uncertain information fusion. Recent advances suggest to …

With the development of quantum decision making, how to bridge classical theory with
quantum framework has gained much attention in past few years. Recently, a complex …

Fuzzy complex event processing-based decision-making systems have received
considerable research interests recently. In particular, a well-managed operator distribution …

Fuzzy membership function plays an important role in fuzzy set theory. However, how to
measure the information volume of fuzzy membership function is still an open issue. The …

Combining basic probability assignments (BPAs) with time series is common in real-life
cases. Therefore, a new evidence fusion approach based on belief entropy and a visibility …