Łukasiewicz逻辑中的单模三角剖分：概率相干性的复杂度界限

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning Pub Date : 2025-09-09 DOI:10.1016/j.ijar.2025.109565

Tommaso Flaminio , Serafina Lapenta , Sebastiano Napolitano

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引用次数: 0

摘要

提出了用无穷值Łukasiewicz逻辑的公式表示的事件上的概率相干问题的np包容性的证明。用几何和组合的方法证明了复杂性界中有一个错误，本文修正了这个错误。实际上，我们提出了两种方法来恢复这种不精确的主张，通过这样做，我们表明，该论文的主要结果确实是有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Unimodular triangulations in Łukasiewicz logic: Complexity bounds of probabilistic coherence

A proof for the NP-containment for the probabilistic coherence problem over events represented by formulas of the infinite-valued Łukasiewicz logic was proposed in [1]. The geometric and combinatorial argument to prove that complexity bound contains a mistake that is fixed in the present paper. Actually we present two ways to restore that imprecise claim and, by doing so, we show that the main result of that paper is indeed valid.

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来源期刊

International Journal of Approximate Reasoning 工程技术-计算机：人工智能

CiteScore

6.90

自引率

12.80%

发文量

170

审稿时长

67 days

期刊介绍： The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.