首页|Study Results from Vali-e-Asr University of Rafsanjan Provide New Insights into Machine Learning (Improving Probabilistic Bisimulation for MDPs Using Machine Le arning)
Study Results from Vali-e-Asr University of Rafsanjan Provide New Insights into Machine Learning (Improving Probabilistic Bisimulation for MDPs Using Machine Le arning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting originating from Vali-e-Asr University of Rafsanjan by NewsRx correspondents, research stated, “The utiliza tion of model checking has been suggested as a formal verification technique for analyzing critical systems.” The news journalists obtained a quote from the research from Vali-e-Asr Universi ty of Rafsanjan: “However , the primary challenge in applying to complex systems is the state space explosion problem. To address this issue , bisimulation mini mization has emerged as a prominent method for reducing the number of states in a system , aiming to overcome the difficulties associated with the state space e xplosion problem. For systems with stochastic behaviors , probabilistic bisimula tion is employed to minimize a given model , obtaining its equivalent form with fewer states. In this paper , we propose a novel technique to partition the stat e space of a given probabilistic model to its bisimulation classes. This techniq ue uses the PRISM program of a given model and constructs some small versions of the model to train a classifier. It then applies supervised machine learning te chniques to approximately classify the related partition.”
Vali-e-Asr University of RafsanjanCybo rgsEmerging TechnologiesMachine Learning
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