Bilal Kartal

1.0k total citations · 1 hit paper
15 papers, 542 citations indexed

About

Bilal Kartal is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Bilal Kartal has authored 15 papers receiving a total of 542 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 2 papers in Molecular Biology. Recurrent topics in Bilal Kartal's work include Reinforcement Learning in Robotics (8 papers), Artificial Intelligence in Games (5 papers) and Evolutionary Algorithms and Applications (3 papers). Bilal Kartal is often cited by papers focused on Reinforcement Learning in Robotics (8 papers), Artificial Intelligence in Games (5 papers) and Evolutionary Algorithms and Applications (3 papers). Bilal Kartal collaborates with scholars based in United States, Austria and Canada. Bilal Kartal's co-authors include Pablo Hernández-Leal, Matthew E. Taylor, Stephen J. Guy, Felipe Leno da Silva, Ioannis Karamouzas, Ernesto Nunes, Maria Gini, Stan Birchfield, A. Kamenev and Artem Molchanov and has published in prestigious journals such as Autonomous Agents and Multi-Agent Systems, arXiv (Cornell University) and 2022 International Conference on Robotics and Automation (ICRA).

In The Last Decade

Bilal Kartal

14 papers receiving 510 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A survey and critique of multiagent deep reinforcement learning

2019 339 citations Pablo Hernández-Leal, Bilal Kartal et al. Autonomous Agents and Multi-Agent Systems profile →

Peers

Countries citing papers authored by Bilal Kartal

Since Specialization

Citations

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

Fields of papers citing papers by Bilal Kartal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bilal Kartal. 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 Bilal Kartal. The network helps show where Bilal Kartal may publish in the future.

Co-authorship network of co-authors of Bilal Kartal

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

All Works

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15 of 15 papers shown

#	Work	Indexed citations
1	PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation 2022 ·2022 International Conference on Robotics and Automation (ICRA) ·A. Kamenev, (unknown), (unknown), (unknown), Bilal Kartal, Artem Molchanov, Stan Birchfield, D. Nistér, Nikolai Smolyanskiy	29
2	A Very Condensed Survey and Critique of Multiagent Deep Reinforcement Learning 2020 ·Adaptive Agents and Multi-Agents Systems ·Pablo Hernández-Leal, Bilal Kartal, Matthew E. Taylor	3
3	Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents 2020 ·Proceedings of the AAAI Conference on Artificial Intelligence ·Felipe Leno da Silva, Pablo Hernández-Leal, Bilal Kartal, Matthew E. Taylor	31
4	Towers of Saliency 2019 ·(unknown), (unknown), Bilal Kartal, Pablo Hernández-Leal, Frank Maurer, Matthew E. Taylor	13
5	A survey and critique of multiagent deep reinforcement learning breakdown → 2019 ·Autonomous Agents and Multi-Agent Systems ·Pablo Hernández-Leal, Bilal Kartal, Matthew E. Taylor	339
6	Agent Modeling as Auxiliary Task for Deep Reinforcement Learning 2019 ·arXiv (Cornell University) ·Pablo Hernández-Leal, Bilal Kartal, Matthew E. Taylor	4
7	Agent Modeling as Auxiliary Task for Deep Reinforcement Learning 2019 ·Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment ·Pablo Hernández-Leal, Bilal Kartal, Matthew E. Taylor	18
8	Is multiagent deep reinforcement learning the answer or the question? A brief survey 2018 ·arXiv (Cornell University) ·Pablo Hernández-Leal, Bilal Kartal, Matthew E. Taylor	30
9	IŞİD’E KATILMA SÜRECİNE PSİKO-SOSYAL BİR BAKIŞ 2017 ·DergiPark (Istanbul University) ·Bilal Kartal	0
10	Data Driven Sokoban Puzzle Generation with Monte Carlo Tree Search 2016 ·Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment ·Bilal Kartal, (unknown), Stephen J. Guy	6
11	Generating Sokoban Puzzle Game Levels with Monte Carlo Tree Search 2016 ·University of Minnesota Digital Conservancy (University of Minnesota) ·Bilal Kartal, (unknown), Stephen J. Guy	3
12	Monte Carlo Tree Search for Multi-Robot Task Allocation 2016 ·Proceedings of the AAAI Conference on Artificial Intelligence ·Bilal Kartal, Ernesto Nunes, (unknown), Maria Gini	21
13	Stochastic Tree Search with Useful Cycles for patrolling problems 2015 ·Bilal Kartal, (unknown), Ioannis Karamouzas, Stephen J. Guy	22
14	User-driven narrative variation in large story domains using monte carlo tree search 2014 ·Adaptive Agents and Multi-Agents Systems ·Bilal Kartal, (unknown), Stephen J. Guy	19
15	Generating Believable Stories in Large Domains 2013 ·Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment ·Bilal Kartal, (unknown), Stephen J. Guy	4

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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