Springer Nature Switzerland AG
1573-7470
Quarterly
1.2
1012-2443
1990
31786576000
Netherlands The
English
YES
Google Scholar
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Annals of Mathematics and Artificial Intelligence is a scholarly journal focusing on the application of mathematical methods to diverse areas of Artificial Intelligence. Appeals to readers using quantitative, combinatorial, logical, algebraic and algorithmic methods in AI. Aims to foster new areas of applied mathematics and strengthen the scientific underpinnings of AI. Features collections of papers focusing on one topic, appearing in volumes or separate issues. Influences the development of new areas of applied mathematics and AI. The scope of Annals of Mathematics and Artificial Intelligence is intended to represent a wide range of topics of concern to scholars applying quantitative, combinatorial, logical, algebraic and algorithmic methods to Artificial Intelligence areas as diverse as decision support, automated deduction, reasoning, knowledge-based systems, machine learning, computer vision, robotics and planning. The journal is aimed at: applied logicians, algorithms and complexity researchers, Artificial Intelligence theorists and applications specialists using mathematical methods. It is hoped to influence the spawning of new areas of applied mathematics and the strengthening of the scientific underpinnings of Artificial Intelligence. Annals of Mathematics and Artificial Intelligence consists of collections of papers appearing either in volumes (400 pages) or in separate issues (100-300 pages). These collections of papers will focus on one topic and will feature one or more guest editors. Potential guest editors are invited to submit their proposal to the Editor-in-Chief. Please note that collections on topics within intelligent systems that show a strong foundational component are strongly encouraged. All information regarding the contents of Annals of Mathematics and Artificial Intelligence should be addressed to the Editor-in-Chief.
Estimating the data density is one of the challenging problem topics in the deep learning society. In this paper, we present a simple yet effective methodology for estimating the data densit...
In this paper we propose a new notion of a clique reliability. The clique reliability is understood as the ratio of the number of statistically significant links in a clique to the number of...
The original version of this Preface article unfortunately contained an incorrect data “Following the conference, authors of selected presentations were invited to submit full journal pap...
In recent years, multi-task learning (MTL) has become a popular field in machine learning and has a key role in various domains. Sharing knowledge across tasks in MTL can improve the perform...
In this paper, we investigate a novel physician scheduling problem in the Mobile Cabin Hospitals (MCH) which are constructed in Wuhan, China during the outbreak of the Covid-19 pandemic. The...
This paper deals with a parallel machine scheduling problem with linearly increasing energy consumption cost. Maintenance activities are considered in the problem. After maintenance, the mac...
Human Learning Optimization (HLO) is a simple yet efficient binary meta-heuristic, in which three learning operators, i.e. the random learning operator (RLO), individual learning operator (I...
This special issue of the Annals of Mathematics and Artificial Intelligence (AMAI) consists of selected thoroughly revised and extended journal papers originating from the LION 15 conferenc...
Aiming to meet increasing energy demand and reduce carbon emissions caused by fossil fuel consumption, China is vigorously supporting the diffusion of photovoltaic (PV) generation equipment....
Building a scalable machine learning system for unsupervised anomaly detection via representation learning is highly desirable. One of the prevalent methods is using a reconstruction error o...
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