上海科技大学知识管理系统

Safety-critical decision-making often necessitates operating within complex and uncertain environments, typically characterized as partially-observable Multi-Agent Systems (MAS). Effective decision-making in these settings demands a profound understanding of the environment and accurate representation and quantification of epistemic uncertainty due to informational deficits. This uncertainty representation must reflect prior knowledge and dynamically integrate observational data. While Version Space Learning (VSL) offers a framework for set-based uncertainty representation, prior methods often depend on under-approximation of the "true" version space, which falls short in safety-critical applications. We introduce a model checking-based VSL framework to enhance uncertainty representation and context-awareness in such applications. Our approach employs network of timed automata (NTA) to model the MAS environment, capturing its mechanisms and prior knowledge efficiently. The inherent non-determinism of timed automata facilitates an over-approximation of the "true" version space, ensuring all plausible hypotheses are considered. We further refine the parameter ranges of the NTA using proof traces, optimizing the use of observational data. In a medical diagnosis case study, our framework identified a missing rule in a traditional rule-based system and provided interpretable and scalable results, demonstrating our method's ability to improve decision-making accuracy and manage complex scenarios effectively.