Linguistic term sets are usually preferred to represent evaluations and preferences in qualitative group decision making (QGDM). Due to the different backgrounds and levels of knowledge of experts, linguistic term sets with different cardinality and/or semantics are used. Hence, it is vital to manage these linguistic terms from distinct information sources in QGDM. In this paper, we present a comprehensive review on relative developments of multi-granularity linguistic term sets by several perspectives, such as the transformation techniques, the aggregation functions, decision making processes and the applications. Finally, some possible directions for future research are pointed out as well.

Artificial intelligence (AI) is the most popular technology for searching the natural essence of human beings' intelligence. AI is influencing the paradigm of the enterprise management and product optimization. The development of AI in enterprise management provide an opportunity for all enterprises to construct automated business processes, improve the customer experience, and expand the product differentiation. Nowadays, the changeable world increases the level of difficulty to make an appropriate AI strategy for a company because of the information uncertainty and complexity. Probabilistic dual hesitant fuzzy set (PDHFS), which is a very effective tool to handle uncertain information, contains the hesitant fuzzy information and the corresponding probabilistic information. Standing in front of the more and more complex evaluation/selection problems, decision makers (DMs) could express their preference information more flexibly using the probabilistic hesitant fuzzy information. In this paper, we focus on the strategy selection problem in AI and solving it by a proposed integrated AHP and VIKOR method under probabilistic dual hesitant fuzzy information. First, we construct the probabilistic dual hesitant fuzzy comparison matrix (PDHFCM) and propose a specific transformation function for using AHP method under probabilistic dual hesitant fuzzy information. For completing the AHP, we redefine a consistency measure and propose an appropriate information-improved approach to obtain the consistent comparison matrix and the corresponding weight values simultaneously. In addition, we study the properties of PDHFS deeply and propose a new comparison method and a novel distance measure for PDHFS to distinguish the different probabilistic hesitant fuzzy information effectively. Then, we propose an integrated VIKOR and AHP method and use the method to solve the AI strategy selection problem. Finally, the availability and effectiveness of the proposed method are illustrated by a case on AI strategy selection.