| 标 题： |
A Formal Account for Reasoning About Uncertain Probabilities in Intelligence Analysis
关于情报分析中不确定概率推理的正式方法 |
| 作 者： |
Pietro Baroni
Federico Cerutti
Massimiliano Giacomin
Lance M. Kaplan
Murat Sensoy |
| 关键词： |
Uncertain probabilities [不确定的概率]
Intelligence analysis [ 情报分析]
Artificial intelligence [ 人工智能] |
| 出版年： |
2023 |
| 文集名： |
2023 IEEE International Workshop on Technologies for Defense and Security (TechDefense 2023)
2023年IEEE国际国防与安全技术研讨会（TechDefense 2023） |
| 会议名： |
2023 IEEE International Workshop on Technologies for Defense and Security (TechDefense 2023)
2023年IEEE国际国防与安全技术研讨会（TechDefense 2023） |
| 届 次： |
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| 摘 要： |
In the sixth assessment of the international panel on climate change (IPCC) one can read sentences such as "cumulative net CO_2 emissions over the last decade (2010-2019) are about the same size as the 11 remaining carbon budget likely to limit warming to 1.5C (medium confidence)." Such reports are very common in the intelligence community, but a formal account for dealing with the degree of belief and of confidence is still missing. In this paper, we propose a formal account for allowing such degrees of belief and the associated confidence to be used to label arguments in abstract argumentation settings. We focus on the task of probabilistic inference over a chosen query building upon Sato's distribution semantics which has been already shown to encompass a variety of cases including the semantics of Bayesian networks. Borrowing from the vast literature on such semantics, we examine how such tasks can be dealt with in practice when considering uncertain probabilities.
在国际气候变化政府间小组（IPCC）第六次评估报告中，可以看到诸如“过去十年（2010–2019年）的累计净二氧化碳排放量与剩余碳预算相当，该预算预计可将升温限制在1.5°C以内（中等置信度）”之类的表述。这类报告在情报界十分常见，但目前仍缺乏一种正式的方法来处理信念程度和置信度。本文提出了一种形式化框架，用于在抽象论证情境中利用这些信念程度及其相应的置信度对论点进行标注。我们聚焦于基于Sato的分布语义模型所构建的查询概率推理任务，该模型已被证明能够涵盖包括贝叶斯网络语义在内的多种情形。借鉴现有大量关于此类语义的研究成果，本文探讨了在考虑不确定概率时，如何在实践中有效处理此类任务。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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