Artificial Intelligence最新文献_第9页

Matching papers and reviewers at large conferences 在大型会议上为论文和审稿人牵线搭桥

IF 14.4 2区计算机科学

Artificial Intelligence Pub Date : 2024-03-25 DOI: 10.1016/j.artint.2024.104119

Kevin Leyton-Brown , Mausam , Yatin Nandwani , Hedayat Zarkoob , Chris Cameron , Neil Newman , Dinesh Raghu

{"title":"Matching papers and reviewers at large conferences","authors":"Kevin Leyton-Brown , Mausam , Yatin Nandwani , Hedayat Zarkoob , Chris Cameron , Neil Newman , Dinesh Raghu","doi":"10.1016/j.artint.2024.104119","DOIUrl":"https://doi.org/10.1016/j.artint.2024.104119","url":null,"abstract":"<div><p>Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferences, the tight timelines on which they operate, and a recent surge in explicitly dishonest behavior, there is now no alternative to performing this matching in an automated way. This paper introduces <em>Large Conference Matching (LCM)</em>, a novel reviewer–paper matching approach that was recently deployed in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), and has since been adopted (wholly or partially) by other conferences including ICML 2022, AAAI 2022-2024, and IJCAI 2022-2024. LCM has three main elements: (1) collecting and processing input data to identify problematic matches and generate reviewer–paper scores; (2) formulating and solving an optimization problem to find good reviewer–paper matchings; and (3) a two-phase reviewing process that shifts reviewing resources away from papers likely to be rejected and towards papers closer to the decision boundary. This paper also describes an evaluation of these innovations based on an extensive post-hoc analysis on real data—including a comparison with the matching algorithm used in AAAI's previous (2020) iteration—and supplements this with additional numerical experimentation.<span><sup>2</sup></span></p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"331 ","pages":"Article 104119"},"PeriodicalIF":14.4,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224000559/pdfft?md5=fb8e284a4c8e25a00c2339ca22f7ea3a&pid=1-s2.0-S0004370224000559-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140537025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Almost proportional allocations of indivisible chores: Computation, approximation and efficiency 几乎按比例分配不可分割的家务：计算、近似和效率

IF 14.4 2区计算机科学

Artificial Intelligence Pub Date : 2024-03-24 DOI: 10.1016/j.artint.2024.104118

Haris Aziz , Bo Li , Hervé Moulin , Xiaowei Wu , Xinran Zhu

引用次数: 0

Knowledge-driven profile dynamics 知识驱动的简介动态

IF 14.4 2区计算机科学

Artificial Intelligence Pub Date : 2024-03-21 DOI: 10.1016/j.artint.2024.104117

Eduardo Fermé , Marco Garapa , Maurício D.L. Reis , Yuri Almeida , Teresa Paulino , Mariana Rodrigues

引用次数: 0

Regular decision processes 常规决策程序

IF 14.4 2区计算机科学

Artificial Intelligence Pub Date : 2024-03-21 DOI: 10.1016/j.artint.2024.104113

Ronen I. Brafman , Giuseppe De Giacomo

引用次数: 0

Lifted algorithms for symmetric weighted first-order model sampling 对称加权一阶模型采样的提升算法

IF 14.4 2区计算机科学

Artificial Intelligence Pub Date : 2024-03-19 DOI: 10.1016/j.artint.2024.104114

Yuanhong Wang , Juhua Pu , Yuyi Wang , Ondřej Kuželka

{"title":"Lifted algorithms for symmetric weighted first-order model sampling","authors":"Yuanhong Wang , Juhua Pu , Yuyi Wang , Ondřej Kuželka","doi":"10.1016/j.artint.2024.104114","DOIUrl":"10.1016/j.artint.2024.104114","url":null,"abstract":"<div><p>Weighted model counting (WMC) is the task of computing the weighted sum of all satisfying assignments (i.e., models) of a propositional formula. Similarly, weighted model sampling (WMS) aims to randomly generate models with probability proportional to their respective weights. Both WMC and WMS are hard to solve exactly, falling under the #<span>P</span>-hard complexity class. However, it is known that the counting problem may sometimes be tractable, if the propositional formula can be compactly represented and expressed in first-order logic. In such cases, model counting problems can be solved in time polynomial in the domain size, and are known as <em>domain-liftable</em>. The following question then arises: Is it also the case for WMS? This paper addresses this question and answers it affirmatively. Specifically, we prove the <em>domain-liftability under sampling</em> for the two-variables fragment of first-order logic with counting quantifiers in this paper, by devising an efficient sampling algorithm for this fragment that runs in time polynomial in the domain size. We then further show that this result continues to hold even in the presence of cardinality constraints. To empirically validate our approach, we conduct experiments over various first-order formulas designed for the uniform generation of combinatorial structures and sampling in statistical-relational models. The results demonstrate that our algorithm outperforms a state-of-the-art WMS sampler by a substantial margin, confirming the theoretical results.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"331 ","pages":"Article 104114"},"PeriodicalIF":14.4,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140182563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Embedding justification theory in approximation fixpoint theory 近似定点理论中的嵌入论证理论

IF 14.4 2区计算机科学

Artificial Intelligence Pub Date : 2024-03-16 DOI: 10.1016/j.artint.2024.104112

Simon Marynissen , Bart Bogaerts , Marc Denecker

引用次数: 0

Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection 对数函数的双曲Secant表示法：将概率多实例学习应用于 CT 颅内出血检测

IF 14.4 2区计算机科学

Artificial Intelligence Pub Date : 2024-03-15 DOI: 10.1016/j.artint.2024.104115

Francisco M. Castro-Macías , Pablo Morales-Álvarez , Yunan Wu , Rafael Molina , Aggelos K. Katsaggelos

{"title":"Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection","authors":"Francisco M. Castro-Macías , Pablo Morales-Álvarez , Yunan Wu , Rafael Molina , Aggelos K. Katsaggelos","doi":"10.1016/j.artint.2024.104115","DOIUrl":"https://doi.org/10.1016/j.artint.2024.104115","url":null,"abstract":"<div><p>Multiple Instance Learning (MIL) is a weakly supervised paradigm that has been successfully applied to many different scientific areas and is particularly well suited to medical imaging. Probabilistic MIL methods, and more specifically Gaussian Processes (GPs), have achieved excellent results due to their high expressiveness and uncertainty quantification capabilities. One of the most successful GP-based MIL methods, VGPMIL, resorts to a variational bound to handle the intractability of the logistic function. Here, we formulate VGPMIL using Pólya-Gamma random variables. This approach yields the same variational posterior approximations as the original VGPMIL, which is a consequence of the two representations that the Hyperbolic Secant distribution admits. This leads us to propose a general GP-based MIL method that takes different forms by simply leveraging distributions other than the Hyperbolic Secant one. Using the Gamma distribution we arrive at a new approach that obtains competitive or superior predictive performance and efficiency. This is validated in a comprehensive experimental study including one synthetic MIL dataset, two well-known MIL benchmarks, and a real-world medical problem. We expect that this work provides useful ideas beyond MIL that can foster further research in the field.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"331 ","pages":"Article 104115"},"PeriodicalIF":14.4,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224000511/pdfft?md5=5d905ec5cc97e9127870dd8564615620&pid=1-s2.0-S0004370224000511-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140162783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A neurosymbolic cognitive architecture framework for handling novelties in open worlds 处理开放世界中新奇事物的神经符号认知架构框架

IF 14.4 2区计算机科学

Artificial Intelligence Pub Date : 2024-03-15 DOI: 10.1016/j.artint.2024.104111

Shivam Goel , Panagiotis Lymperopoulos , Ravenna Thielstrom , Evan Krause , Patrick Feeney , Pierrick Lorang , Sarah Schneider , Yichen Wei , Eric Kildebeck , Stephen Goss , Michael C. Hughes , Liping Liu , Jivko Sinapov , Matthias Scheutz

{"title":"A neurosymbolic cognitive architecture framework for handling novelties in open worlds","authors":"Shivam Goel , Panagiotis Lymperopoulos , Ravenna Thielstrom , Evan Krause , Patrick Feeney , Pierrick Lorang , Sarah Schneider , Yichen Wei , Eric Kildebeck , Stephen Goss , Michael C. Hughes , Liping Liu , Jivko Sinapov , Matthias Scheutz","doi":"10.1016/j.artint.2024.104111","DOIUrl":"10.1016/j.artint.2024.104111","url":null,"abstract":"<div><p>“Open world” environments are those in which novel objects, agents, events, and more can appear and contradict previous understandings of the environment. This runs counter to the “closed world” assumption used in most AI research, where the environment is assumed to be fully understood and unchanging. The types of environments AI agents can be deployed in are limited by the inability to handle the novelties that occur in open world environments. This paper presents a novel cognitive architecture framework to handle open-world novelties. This framework combines symbolic planning, counterfactual reasoning, reinforcement learning, and deep computer vision to detect and accommodate novelties. We introduce general algorithms for exploring open worlds using inference and machine learning methodologies to facilitate novelty accommodation. The ability to detect and accommodate novelties allows agents built on this framework to successfully complete tasks despite a variety of novel changes to the world. Both the framework components and the entire system are evaluated in Minecraft-like simulated environments. Our results indicate that agents are able to efficiently complete tasks while accommodating “concealed novelties” not shared with the architecture development team.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"331 ","pages":"Article 104111"},"PeriodicalIF":14.4,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140182567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A differentiable first-order rule learner for inductive logic programming 用于归纳逻辑编程的可微分一阶规则学习器

IF 14.4 2区计算机科学

Artificial Intelligence Pub Date : 2024-03-15 DOI: 10.1016/j.artint.2024.104108

Kun Gao , Katsumi Inoue , Yongzhi Cao , Hanpin Wang

{"title":"A differentiable first-order rule learner for inductive logic programming","authors":"Kun Gao , Katsumi Inoue , Yongzhi Cao , Hanpin Wang","doi":"10.1016/j.artint.2024.104108","DOIUrl":"https://doi.org/10.1016/j.artint.2024.104108","url":null,"abstract":"<div><p>Learning first-order logic programs from relational facts yields intuitive insights into the data. Inductive logic programming (ILP) models are effective in learning first-order logic programs from observed relational data. Symbolic ILP models support rule learning in a data-efficient manner. However, symbolic ILP models are not robust to learn from noisy data. Neuro-symbolic ILP models utilize neural networks to learn logic programs in a differentiable manner which improves the robustness of ILP models. However, most neuro-symbolic methods need a strong language bias to learn logic programs, which reduces the usability and flexibility of ILP models and limits the logic program formats. In addition, most neuro-symbolic ILP methods cannot learn logic programs effectively from both small-size datasets and large-size datasets such as knowledge graphs. In the paper, we introduce a novel differentiable ILP model called differentiable first-order rule learner (DFORL), which is scalable to learn rules from both smaller and larger datasets. Besides, DFORL only needs the number of variables in the learned logic programs as input. Hence, DFORL is easy to use and does not need a strong language bias. We demonstrate that DFORL can perform well on several standard ILP datasets, knowledge graphs, and probabilistic relation facts and outperform several well-known differentiable ILP models. Experimental results indicate that DFORL is a precise, robust, scalable, and computationally cheap differentiable ILP model.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"331 ","pages":"Article 104108"},"PeriodicalIF":14.4,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140190964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-deterministic approximation fixpoint theory and its application in disjunctive logic programming 非确定性近似定点理论及其在断据逻辑编程中的应用

IF 14.4 2区计算机科学

Artificial Intelligence Pub Date : 2024-03-08 DOI: 10.1016/j.artint.2024.104110

Jesse Heyninck , Ofer Arieli , Bart Bogaerts

引用次数: 0