Abstract: Intelligent gaming is a challenging problem in the field of cognitive decision-making intelligence, and it is the key support for assisting joint combat planning and intelligent mission planning. The intelligent gaming model is sorted out from three perspectives: collaborative team game, competitive zero-sum game and mixed general-sum game, four kinds of cognitive models of intelligent gaming are defined from the perspective of cognition: operational game （complete or bounded rationality）, uncertain game （experience /knowledge）, emerging exploratory game （intuition and inspiration）, and population interactive game （co-evolution）. Solutions of intelligent gaming are given from three perspectives: trustworthy solution of problems, benchmark learning method, and strategy training platform. Secondly, based on Transformer framework, the decision-making Transformer methods are analyzed from architecture enhancement （presentation learning, network combination, model extension）and sequence modeling （offline pre-training, online adaptation, model extension）. Relevant research provides an initial reference for the construction of decision-making pre-trained model in multi-agent, multi-task, multi-mode and sim-to-real transfer application scenarios under the paradigm of "offline pre-training + online adaptation". It is expected to provide feasible reference for the research on the decision-making foundation model in the field of intelligent gaming.