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Prescriptive Inductive Operations on Probabilities Serving to Decision-Making Agents
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DOI：10.1109/TSMC.2020.3047992.png)
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摘要
En 中文
Approximation, extension, and merging of probability distributions support inductive reasoning. They serve to modeling, knowledge, and preference elicitation as well as to a soft cooperation within various decision-making (DM) scenarios. The theory dubbed as the fully probabilistic design of DM strategies unifies the design of these operations on distributions. The unification decreases the danger of their improper choice and use. Still there is an uncertainty how the gained tools should be wielded. This article diminishes it by spelling out conditions ruling their exploitation. This article serves as an updated description of these tools, provides examples of their use, and guides their tailoring to diverse scenarios.
Keyword:
Tools
Task analysis
Uncertainty
Merging
Cognition
Probabilistic logic
Entropy
Approximation
dynamic decision making (DM)
extension
merging
relative entropy (RE)
uncertainty
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对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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