A logic of probabilistic knowledge and strategy
The ability of reasoning about knowledge and strategy is key to the autonomy of an intelligent system of multiple players. In this paper, we study the logic of knowledge and strategy in stochastic multi-agent systems, where the system's behaviour is determined by both the behaviour of the players and by some random elements. Players have incomplete information about the system and do not have memory. A logic PATEL*, whose semantics is based on partially-observed concurrent game structures, is proposed. The computational complexities of model checking the logic and its sublogics are solved.
This article introduces improving Apriori parallel algorithm based on correlative rule,judgement tree aposteriori algorithm based on classification and K-cluster algorithm based on clustering,and applied these algorithms into intelligent information processing course synthetically.