Michail G. Lagoudakis

48 papers receiving 1.3k citations

Peers

Michail G. Lagoudakis
Comparison fields: 5 of 123
  • Artificial Intelligence 582
  • Computer Networks and Communications 493
  • Computer Vision and Pattern Recognition 251
  • Management Science and Operations Research 236
  • Control and Systems Engineering 212
Replace Matthijs T. J. Spaan with:
Matthijs T. J. Spaan Netherlands
Mieczyslaw M. Kokar United States
Hongwei Ge China
Amar Ramdane-Chérif France
Gabriel Dulac-Arnold United Kingdom
Hamzeh Alabool Saudi Arabia
Maryam Kamgarpour Switzerland
Malik Ghallab France
Broderick Crawford Chile
Yinan Guo China
Michail G. Lagoudakis relative to Matthijs T. J. Spaan Netherlands Matthijs T. J. Spaan's profile →
Citations per field
00.5×1.5×
Matthijs T. J. Spaan · 1×
Citations per year

Countries citing papers authored by Michail G. Lagoudakis

Since Specialization
Citations

This map shows the geographic impact of Michail G. Lagoudakis'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 Michail G. Lagoudakis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michail G. Lagoudakis more than expected).

Fields of papers citing papers by Michail G. Lagoudakis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michail G. Lagoudakis. 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 Michail G. Lagoudakis. The network helps show where Michail G. Lagoudakis may publish in the future.

Co-authorship network of co-authors of Michail G. Lagoudakis

This figure shows the co-authorship network connecting the top 25 collaborators of Michail G. Lagoudakis. A scholar is included among the top collaborators of Michail G. Lagoudakis 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 Michail G. Lagoudakis. Michail G. Lagoudakis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 14
2 53
3 11
4 5
5 20
6
On the Locality of Action Domination in Sequential Decision Making
6
7 12
8 13
9
Algorithms and Bounds for Sampling-based Approximate Policy Iteration
1
10
Reinforcement learning as classification: leveraging modern classifiers
60
11
Approximate policy iteration using large-margin classifiers
0
12
Coordinated Reinforcement Learning
182
13
Learning in Zero-Sum Team Markov Games Using Factored Value Functions
11
14
Model-Free Least-Squares Policy Iteration
48
15
Selecting the Right Algorithm
12
16
Reinforcement Learning for Algorithm Selection
7
17
Algorithm Selection using Reinforcement Learning
81
18
Robot navigation with a polar neural map
3
19
Universal Access to Mobile Computing Devices through Speech Input
7
20 2

About Michail G. Lagoudakis

Michail G. Lagoudakis is a scholar working on Artificial Intelligence, Software and Computational Theory and Mathematics, having authored 51 papers that have together received 1.4k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (16 papers), Evolutionary Algorithms and Applications (7 papers) and Optimization and Search Problems (6 papers). The work is most often cited by research in Computer Networks and Communications (493 citations), Management Science and Operations Research (236 citations) and Artificial Intelligence (582 citations). Michail G. Lagoudakis has collaborated with scholars based in Greece, United States and Austria. Frequent co-authors include Ronald Parr, Michael L. Littman, Carlos Guestrin, Pınar Keskinocak, Anton J. Kleywegt, Sven Koenig, Craig A. Tovey, Adam Meyerson, David Kempe and Sonal Jain. Their work appears in journals such as IEEE Transactions on Instrumentation and Measurement, Applied Sciences and IEEE Signal Processing Letters.

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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