Lifted inference beyond first-order logic

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Pub Date : 2025-02-24 DOI:10.1016/j.artint.2025.104310
Sagar Malhotra , Davide Bizzaro , Luciano Serafini
{"title":"Lifted inference beyond first-order logic","authors":"Sagar Malhotra ,&nbsp;Davide Bizzaro ,&nbsp;Luciano Serafini","doi":"10.1016/j.artint.2025.104310","DOIUrl":null,"url":null,"abstract":"<div><div>Weighted First Order Model Counting (WFOMC) is fundamental to probabilistic inference in statistical relational learning models. As WFOMC is known to be intractable in general (#P-complete), logical fragments that admit polynomial time WFOMC are of significant interest. Such fragments are called <em>domain liftable</em>. Recent works have shown that the two-variable fragment of first order logic extended with counting quantifiers (C<sup>2</sup>) is domain-liftable. However, many properties of real-world data, like <em>acyclicity</em> in citation networks and <em>connectivity</em> in social networks, cannot be modeled in C<sup>2</sup>, or first order logic in general. In this work, we expand the domain liftability of C<sup>2</sup> with multiple such properties. We show that any C<sup>2</sup> sentence remains domain liftable when one of its relations is restricted to represent a directed acyclic graph, a connected graph, a tree (resp. a directed tree) or a forest (resp. a directed forest). All our results rely on a novel and general methodology of <em>counting by splitting</em>. Besides their application to probabilistic inference, our results provide a general framework for counting combinatorial structures. We expand a vast array of previous results in discrete mathematics literature on directed acyclic graphs, phylogenetic networks, etc.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"342 ","pages":"Article 104310"},"PeriodicalIF":5.1000,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0004370225000293","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Weighted First Order Model Counting (WFOMC) is fundamental to probabilistic inference in statistical relational learning models. As WFOMC is known to be intractable in general (#P-complete), logical fragments that admit polynomial time WFOMC are of significant interest. Such fragments are called domain liftable. Recent works have shown that the two-variable fragment of first order logic extended with counting quantifiers (C2) is domain-liftable. However, many properties of real-world data, like acyclicity in citation networks and connectivity in social networks, cannot be modeled in C2, or first order logic in general. In this work, we expand the domain liftability of C2 with multiple such properties. We show that any C2 sentence remains domain liftable when one of its relations is restricted to represent a directed acyclic graph, a connected graph, a tree (resp. a directed tree) or a forest (resp. a directed forest). All our results rely on a novel and general methodology of counting by splitting. Besides their application to probabilistic inference, our results provide a general framework for counting combinatorial structures. We expand a vast array of previous results in discrete mathematics literature on directed acyclic graphs, phylogenetic networks, etc.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
加权一阶模型计数(WFOMC)是统计关系学习模型中概率推断的基础。众所周知,WFOMC 在一般情况下是难以实现的(#P-complete),因此,能够实现多项式时间 WFOMC 的逻辑片段就引起了人们的极大兴趣。这种片段被称为可域提升片段。最近的研究表明,用计数量词扩展的一阶逻辑的双变量片段(C2)是可域提升的。然而,现实世界数据的许多属性,如引文网络中的非循环性和社交网络中的连通性,无法用 C2 或一般一阶逻辑建模。在这项工作中,我们用多个此类属性扩展了 C2 的域可提升性。我们证明,当任何 C2 句子的一个关系被限制为表示有向无环图、连通图、树(有向树)或森林(有向森林)时,它仍然是可域提升的。我们的所有结果都依赖于一种新颖而通用的拆分计数方法。除了应用于概率推理,我们的结果还为组合结构计数提供了一个通用框架。我们扩展了以前离散数学文献中关于有向无环图、系统发育网络等的大量结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Artificial Intelligence
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.
期刊最新文献
Editorial Board Lifted inference beyond first-order logic (Re)Conceptualizing trustworthy AI: A foundation for change Grounded predictions of teamwork as a one-shot game: A multiagent multi-armed bandits approach Stochastic population update can provably be helpful in multi-objective evolutionary algorithms
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1