fuzzy computation tree logic is an extension of classical temporal logiccomputationtreelogic, which is used to specify the properties of systems with uncertain information content. This paper investigates the robus...
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ISBN:
(纸本)9783319462066;9783319462059
fuzzy computation tree logic is an extension of classical temporal logiccomputationtreelogic, which is used to specify the properties of systems with uncertain information content. This paper investigates the robustness of fuzzy computation tree logic. Robustness results are proved based on complete Heyting algebra and standard Lukasiewicz algebra.
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 util...
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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.
Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property *** there is an extensive literature on qualitative properties such as safety and liveness,t...
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Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property *** there is an extensive literature on qualitative properties such as safety and liveness,there is still a lack of quantitative and uncertain property verifications for these *** uncertain environments,agents must make judicious decisions based on subjective *** verify epistemic and measurable properties in multi-agent systems,this paper extends fuzzy computation tree logic by introducing epistemic modalities and proposing a new fuzzy computation tree logic of Knowledge(FCTLK).We represent fuzzy multi-agent systems as distributed knowledge bases with fuzzy epistemic interpreted *** addition,we provide a transformation algorithm from fuzzy epistemic interpreted systems to fuzzy Kripke structures,as well as transformation rules from FCTLK formulas to fuzzy computation tree logic(FCTL)***,we transform the FCTLK model checking problem into the FCTL model *** enables the verification of FCTLK formulas by using the fuzzy model checking algorithm of FCTL without additional computational ***,we present correctness proofs and complexity analyses of the proposed ***,we further illustrate the practical application of our approach through an example of a train control system.
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