关于非二元定性概率网络中不正确推论的说明

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence Pub Date : 2024-07-14 DOI:10.1016/j.artint.2024.104180

Jack Storror Carter

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引用次数: 0

摘要

定性概率网络（QPN）结合了贝叶斯网络的条件独立性假设和正负依赖性的定性特性。定性概率网络将正相关性的各种直观特性形式化，允许对大型变量网络进行推断。然而，我们将在本文中证明，由于不正确的对称属性，在非二元 QPN 中得到的许多推论在数学上并不正确。我们将举例说明这种不正确的推论，并简要讨论可能的解决方法。

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A note on incorrect inferences in non-binary qualitative probabilistic networks

Qualitative probabilistic networks (QPNs) combine the conditional independence assumptions of Bayesian networks with the qualitative properties of positive and negative dependence. They formalise various intuitive properties of positive dependence to allow inferences over a large network of variables. However, we will demonstrate in this paper that, due to an incorrect symmetry property, many inferences obtained in non-binary QPNs are not mathematically true. We will provide examples of such incorrect inferences and briefly discuss possible resolutions.

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来源期刊

Artificial Intelligence 工程技术-计算机：人工智能

CiteScore

11.20

自引率

1.40%

发文量

118

审稿时长

8 months

期刊介绍： The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.