Modeling Fuzzy Belief with Plausibility Degree in Intelligent Systems

Belief plays an important part in intelligent systems modeling. On the one hand, in the multi-agent systems community, belief is usually modeled as a mental state of agents by modal logic in the context of BDI (belief-desire intention). On the other hand, in epistemic logic research, belief and knowledge are often taken into account together, which are main components of knowledge-based systems. However problems such as logical omniscience and several others come along with this one still left unsettled since agents are resource bounded reasoners. In this paper, the construction of fuzzy belief logic based on plausibility degree is considered, three kinds of fuzzy belief operators (individual fuzzy belief, group fuzzy belief, and common fuzzy belief) are investigated, and the associated Kripke semantics is given. Logical omniscience problem of belief modeling is done away with and proved. So that a new powerful tool for intelligent systems modeling is well constructured.