Annals of Mathematics and Artificial Intelligence

Aims & Scope

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.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1
2024	1.20

SJR (SCImago Journal Rank)

Year	Value
2024	0.419

Quartile

Year	Value
2024	Q3

Journal Rank

Year	Value
2024	13364

Journal Citation Indicator

Year	Value
2024	232

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Coverage for robotics – A survey of recent results

  Citation: 914

  Authors: Howie

• Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines

  Citation: 712

  Authors: Tim, Roger M.

• Theoretical Comparison between the Gini Index and Information Gain Criteria

  Citation: 503

  Authors: Laura Elena, Kilian

• Logic programs with stable model semantics as a constraint programming paradigm

  Citation: 446

  Authors: Ilkka

• Possibility Theory, Probability Theory and Multiple-Valued Logics: A Clarification

  Citation: 302

  Authors: Didier, Henri

• Modeling genetic algorithms with Markov chains

  Citation: 291

  Authors: Allen E., Michael D.

• Spanning-tree based coverage of continuous areas by a mobile robot

  Citation: 244

  Authors: Yoav, Elon

• A Reasoning Model Based on the Production of Acceptable Arguments

  Citation: 241

  Authors: Leila, Claudette

• On the computational cost of disjunctive logic programming: Propositional case

  Citation: 201

  Authors: Thomas, Georg

• Bayesian optimization for learning gaits under uncertainty

  Citation: 184

  Authors: Roberto, André, Jan, Marc Peter

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