Abraham Prieto

513 citations
28 papers · 217 · h-index 8

Impact in

Papers in

    • Reinforcement Learning in Robotics 15
    • Evolutionary Algorithms and Applications 10
    • AI-based Problem Solving and Planning 5
    • Metaheuristic Optimization Algorithms Research 5
    • Evolutionary Game Theory and Cooperation 5

Abraham Prieto

28 papers receiving 204 citations

Peers

Abraham Prieto
Comparison fields: 5 of 45
  • Artificial Intelligence 120
  • Computer Science Applications 15
  • Media Technology 21
  • Computer Vision and Pattern Recognition 35
  • Mechanical Engineering 62
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Citations per year

Countries citing papers authored by Abraham Prieto

Since Specialization
Citations

This map shows the geographic impact of Abraham Prieto's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Abraham Prieto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Abraham Prieto more than expected).

Fields of papers citing papers by Abraham Prieto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Abraham Prieto. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Abraham Prieto. The network helps show where Abraham Prieto may publish in the future.

Co-authors

The 11 scholars most cited alongside Abraham Prieto, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Abraham Prieto Line = papers co-authored together Abraham Prieto links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201859
2 201029
3 201817
4 201615
5 201014
6 201214
7 201710
8 20169
9 20197
10 19816
11 20154
12 20114
13 20184
14 20203
15 20163
16 20172
17 20112
18 20132
19 20162
20 20172

About Abraham Prieto

Abraham Prieto is a scholar working on Artificial Intelligence, Sociology and Political Science, Mechanical Engineering, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 28 papers that have together received 217 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (15 papers), Evolutionary Algorithms and Applications (10 papers), Modular Robots and Swarm Intelligence (5 papers), AI-based Problem Solving and Planning (5 papers), Metaheuristic Optimization Algorithms Research (5 papers), Evolutionary Game Theory and Cooperation (5 papers), Teaching and Learning Programming (3 papers) and Distributed Control Multi-Agent Systems (3 papers). The work is most often cited by research in Artificial Intelligence (120 citations), Computer Science Applications (15 citations), Media Technology (21 citations), Computer Vision and Pattern Recognition (35 citations) and Mechanical Engineering (62 citations). Abraham Prieto has collaborated with scholars based in Spain, France and Netherlands. Frequent co-authors include Francisco Bellas, Richard J. Duro, Nicolas Bredèche, Evert Haasdijk, Rodrigo Salgado, J. A. Becerra, Fernando López Peña, J. A. Méndez, Javier Santamaría and J. Bescós. Their work appears in journals such as Robotics and Autonomous Systems, Integrated Computer-Aided Engineering, Journal of Intelligent & Fuzzy Systems, Neurocomputing and Information Sciences.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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