Model checking fuzzy computation tree logic of multi-agent systems based on fuzzy interpreted systems

Effective communication among autonomous agents is crucial for coordination and solving complex tasks within multi-agent systems. To formalize interactions between agents, social accessibility relations are often utilized. Current research employs model checking algorithms to verificate social commitment properties in multi-agent systems. In fuzzy multi-agent systems, direct quantification and computation of commitment attributes pose challenges. This paper introduces an indirect fuzzy model checking algorithm designed to convert social commitments in uncertain scenarios into quantifiable attributes for verification. Firstly, we propose a fuzzy communicative interpreted system model to represent multi-agent systems with uncertain communication. We then improve fuzzy computation tree logic by adding modalities for commitments and fulfillment, resulting in a fuzzy computation tree logic with commitments for describing system properties related to commitments. A fuzzy model checking algorithm is subsequently presented. This algorithm converts the task of model checking fuzzy computation tree logic with commitments based on fuzzy interpreted systems into model checking fuzzy computation tree logic based on fuzzy Kripke structures. We conclude by providing proofs of correctness and complexity analysis of our algorithm. Furthermore, we demonstrate the effectiveness of our approach for model checking social commitments under fuzzy conditions through simulation experiments on an online shopping system.