Annals of Mathematics and Artificial Intelligence最新文献

35 years of math and AI

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2025-02-10 DOI: 10.1007/s10472-025-09969-7

Martin Charles Golumbic

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The future starts now

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2025-02-05 DOI: 10.1007/s10472-025-09970-0

Jürgen Dix, Michael Fisher

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Guest editorial: Revised selected papers from the LION 16 conference

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2025-01-06 DOI: 10.1007/s10472-024-09958-2

Ilias S. Kotsireas, Panos M. Pardalos

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Common equivalence and size of forgetting from Horn formulae 霍恩公式中常见的等效和遗忘量

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-10-29 DOI: 10.1007/s10472-024-09955-5

Paolo Liberatore

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Time-penalised trees (TpT): introducing a new tree-based data mining algorithm for time-varying covariates 时变树（TpT）：为时变协变量引入一种新的基于树的数据挖掘算法

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-08-22 DOI: 10.1007/s10472-024-09950-w

Mathias Valla

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引用次数: 0

Conformal test martingales for hypergraphical models 超图模型的共形检验马氏体

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-08-03 DOI: 10.1007/s10472-024-09951-9

Ilia Nouretdinov

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Advances in preference handling: foreword 偏好处理的进展：前言

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-07-27 DOI: 10.1007/s10472-024-09954-6

Khaled Belahcène, Sébastien Destercke, Christophe Labreuche, Meltem Öztürk, Paolo Viappiani

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Costly information providing in binary contests 在二进制竞赛中提供昂贵的信息

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-07-27 DOI: 10.1007/s10472-024-09953-7

Noam Simon, Priel Levy, David Sarne

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Tumato 2.0 - a constraint-based planning approach for safe and robust robot behavior Tumato 2.0--一种基于约束的规划方法，可实现安全、稳健的机器人行为

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-07-26 DOI: 10.1007/s10472-024-09949-3

Jan Vermaelen, Tom Holvoet

{"title":"Tumato 2.0 - a constraint-based planning approach for safe and robust robot behavior","authors":"Jan Vermaelen, Tom Holvoet","doi":"10.1007/s10472-024-09949-3","DOIUrl":"https://doi.org/10.1007/s10472-024-09949-3","url":null,"abstract":"<p>Ensuring the safe and effective operation of autonomous systems is a complex undertaking that inherently relies on underlying decision-making processes. To rigorously analyze these processes, formal verification methods, such as model checking, offer a valuable means. However, the non-deterministic nature of realistic environments makes these approaches challenging and often impractical. This work explores the capabilities of a constraint-based planning approach, Tumato, in generating policies that guide the system to predefined goals while adhering to safety constraints. Constraint-based planning approaches are inherently able to provide guarantees of soundness and completeness. Our primary contribution lies in extending Tumato’s capabilities to accommodate non-deterministic outcomes of actions, enhancing the robustness of the behavior. Originally designed to accommodate only deterministic outcomes, actions can now be modeled to include alternative outcomes to address contingencies explicitly. The adapted solver generates policies that enable reaching the goals in a safe manner, even when such alternative outcomes of actions occur. Additionally, we introduce a purely declarative manner for specifying safety in Tumato to further enhance its expressiveness as well as to reduce the susceptibility to errors during specification. The incorporation of cost or duration values to actions enables the solver to restore safety in the most preferred manner when necessary. Finally, we highlight the overlap of Tumato’s safety-related capabilities with a systems-theoretic approach, STPA (Systems-Theoretic Process Analysis). The aim is to emphasize the ability to avoid unsafe control actions without their explicit identification, contributing to a more comprehensive and holistic understanding of safety.</p>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"44 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Calibration methods in imbalanced binary classification 不平衡二元分类中的校准方法

IF 1.2 4区计算机科学

Annals of Mathematics and Artificial Intelligence Pub Date : 2024-07-19 DOI: 10.1007/s10472-024-09952-8

Théo Guilbert, Olivier Caelen, Andrei Chirita, Marco Saerens

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