| Grid path planning with deep reinforcement learning: Preliminary results AI Panov, KS Yakovlev, R Suvorov Procedia computer science 123, 347-353, 2018 | 200 | 2018 |
| Знаковая картина мира субъекта поведения ГС Осипов, АИ Панов, НВ Чудова, ЮМ Кузнецова Физматлит Физматлит Издательство" Физматлит", 2018 | 103* | 2018 |
| Behavior control as a function of consciousness. I. World model and goal setting GS Osipov, AI Panov, NV Chudova Journal of Computer and Systems Sciences International 53, 517-529, 2014 | 83* | 2014 |
| Multilayer cognitive architecture for UAV control S Emel’yanov, D Makarov, AI Panov, K Yakovlev Cognitive Systems Research 39, 58-72, 2016 | 62 | 2016 |
| Behavior control as a function of consciousness. II. Synthesis of a behavior plan GS Osipov, AI Panov, NV Chudova Journal of Computer and Systems Sciences International 54, 882-896, 2015 | 59* | 2015 |
| Real-time object navigation with deep neural networks and hierarchical reinforcement learning A Staroverov, DA Yudin, I Belkin, V Adeshkin, YK Solomentsev, AI Panov IEEE Access 8, 195608-195621, 2020 | 52 | 2020 |
| Object detection with deep neural networks for reinforcement learning in the task of autonomous vehicles path planning at the intersection DA Yudin, A Skrynnik, A Krishtopik, I Belkin, AI Panov Optical Memory and Neural Networks 28, 283-295, 2019 | 48 | 2019 |
| Behavior and path planning for the coalition of cognitive robots in smart relocation tasks AI Panov, K Yakovlev Robot Intelligence Technology and Applications 4: Results from the 4th …, 2017 | 43 | 2017 |
| Behavior planning of intelligent agent with sign world model AI Panov Biologically inspired cognitive architectures 19, 21-31, 2017 | 42 | 2017 |
| Goal setting and behavior planning for cognitive agents AI Panov Scientific and Technical Information Processing 46, 404-415, 2019 | 41* | 2019 |
| Interactive grounded language understanding in a collaborative environment: Iglu 2021 J Kiseleva, Z Li, M Aliannejadi, S Mohanty, M ter Hoeve, M Burtsev, ... NeurIPS 2021 Competitions and Demonstrations Track, 146-161, 2022 | 37* | 2022 |
| Relationships and operations in a sign-based world model of the actor GS Osipov, AI Panov Scientific and Technical Information Processing 45, 317-330, 2018 | 36* | 2018 |
| Hierarchical deep q-network from imperfect demonstrations in minecraft A Skrynnik, A Staroverov, E Aitygulov, K Aksenov, V Davydov, AI Panov Cognitive Systems Research 65, 74-78, 2021 | 35 | 2021 |
| Forgetful experience replay in hierarchical reinforcement learning from expert demonstrations A Skrynnik, A Staroverov, E Aitygulov, K Aksenov, V Davydov, AI Panov Knowledge-Based Systems 218, 106844, 2021 | 34* | 2021 |
| Архитектура многоуровневой интеллектуальной системы управления беспилотными летательными аппаратами ДА Макаров, АИ Панов, КС Яковлев Искусственный интеллект и принятие решений, 18-33, 2015 | 31* | 2015 |
| Extraction of cause-effect relationships from psychological test data using logical methods AI Panov Scientific and Technical Information Processing 41, 275-282, 2014 | 26* | 2014 |
| Hybrid policy learning for multi-agent pathfinding A Skrynnik, A Yakovleva, V Davydov, K Yakovlev, AI Panov IEEE Access 9, 126034-126047, 2021 | 24 | 2021 |
| Sign-based approach to the task of role distribution in the coalition of cognitive agents GA Kiselev, AI Panov Informatics and Automation 57, 161-187, 2018 | 24* | 2018 |
| Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning A Skrynnik, A Andreychuk, M Nesterova, K Yakovlev, A Panov The AAAI Conference on Artificial Intelligence (AAAI 2024), 2023 | 23* | 2023 |
| Interactive grounded language understanding in a collaborative environment: Retrospective on iglu 2022 competition J Kiseleva, A Skrynnik, A Zholus, S Mohanty, N Arabzadeh, MA Côté, ... NeurIPS 2022 Competition Track, 204-216, 2023 | 22* | 2023 |
Константин Сергеевич Яковлев (Kon...ФИЦ ИУ РАН, ВШЭ, МФТИ, AIRIZweryfikowany adres z isa.ru