Laurent Garcia
- Artificial Intelligence top 10%
- Computational Theory and Mathematics
- Management Science and Operations Research
- Computer Networks and Communications
- Signal Processing
- Co-authors
- Pascal NicolasSalem BenferhatRégis SabbadinDidier DuboisHenri PradeOdile PapiniJean-François Baget
- Topics
- Logic, Reasoning, and Knowledge (10 papers)Multi-Agent Systems and Negotiation (7 papers)Bayesian Modeling and Causal Inference (5 papers)
- Cited by
- Artificial IntelligenceManagement Science and Operations ResearchComputational Theory and Mathematics
In The Last Decade
Laurent Garcia
12 papers receiving 121 citations
Peers
Comparison fields: 5 of 23
- Artificial Intelligence 121
- Computational Theory and Mathematics 25
- Management Science and Operations Research 25
- Computer Networks and Communications 16
- Signal Processing 16
Countries citing papers authored by Laurent Garcia
This map shows the geographic impact of Laurent Garcia'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 Laurent Garcia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Laurent Garcia more than expected).
Fields of papers citing papers by Laurent Garcia
This network shows the impact of papers produced by Laurent Garcia. 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 Laurent Garcia. The network helps show where Laurent Garcia may publish in the future.
Co-authorship network of co-authors of Laurent Garcia
This figure shows the co-authorship network connecting the top 25 collaborators of Laurent Garcia. A scholar is included among the top collaborators of Laurent Garcia based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Laurent Garcia. Laurent Garcia is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 12 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 9 | |
| 6 | 3 | |
| 7 | Possibilistic Influence Diagrams | 10 |
| 8 | 36 | |
| 9 | 14 | |
| 10 | Programmation par ensembles-réponses possibilistes. | 0 |
| 11 | 33 | |
| 12 | 7 | |
| 13 | 2 |
About Laurent Garcia
Laurent Garcia is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computational Theory and Mathematics, having authored 13 papers that have together received 130 indexed citations. Recurring topics across this work include Logic, Reasoning, and Knowledge (10 papers), Multi-Agent Systems and Negotiation (7 papers) and Bayesian Modeling and Causal Inference (5 papers). The work is most often cited by research in Artificial Intelligence (121 citations), Management Science and Operations Research (25 citations) and Computational Theory and Mathematics (25 citations). Laurent Garcia has collaborated with scholars based in France and Morocco. Frequent co-authors include Pascal Nicolas, Salem Benferhat, Régis Sabbadin, Didier Dubois, Henri Prade, Odile Papini and Jean-François Baget. Their work appears in journals such as Artificial Intelligence, International Journal of Approximate Reasoning and Journal of Artificial Intelligence Research.
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.