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Ordinal scale based uncertainty models for AI
مدل عدم قطعیت مبتنی بر مقیاس ترتیبی برای هوش مصنوعی-2020 In human processed AI, HP-AI, we build our AI systems based on knowledge learned by human experts rather then that learned by artificial neural networks such as in the case of deep learning. The information provided by these human experts is typically linguistically expressed. In support of HP-AI we look at the properties of an ordinal scale, S , needed to model linguistically expressed quantitative information. Since fuzzy measures provide a very general structure for modeling uncertainty we look at ordinal fuzzy measures. We look at the Sugeno integral based on this ordinal S scale. We discuss the modeling of information about an uncertain variable using an ordinal scale. We look at the problem of multi-source in this ordinal environment. Keywords: Linguistically expressed | Fuzzy measure | Ordinal information | Multi-source fusion |
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