Papers

5

Total Citations

31

H-Index

4

About

Inna Pshenokova is a researcher specializing in artificial intelligence, multi-agent systems, and autonomous robotics, with a particular focus on neurocognitive architectures that draw inspiration from the neural activity of the human brain. Her work bridges cognitive science and applied AI, developing intelligent systems capable of sophisticated decision-making, situational analysis, and natural language understanding in autonomous contexts. Pshenokova's most influential contribution, a 2019 simulation model for static object recognition using machine-learning multi-agent architectures (11 citations), established a foundation for her ongoing research into brain-inspired computational frameworks. She has since expanded this work to encompass situational analysis in intelligent control systems, algorithms for autonomous robot simulation, and natural language processing for mission interpretation in robotics. Her 2023 work on an intelligent spraying system for agricultural robots (6 citations) demonstrates the real-world applicability of her theoretical frameworks, translating neurocognitive principles into practical autonomous solutions. With a cumulative citation count of over 30 across her key publications, Pshenokova has established herself as a meaningful contributor to the field of intelligent autonomous systems, offering a distinctive biologically informed perspective that continues to gain recognition within the robotics and AI research communities.

Research Focus

Key Achievements

4
H-Index
5
Papers
31
Total Citations
6
Avg Citations/Paper
🏆 Most Cited Paper
A Simulation Model for the Cognitive Function of Static Objects Recognition Based on Machine-Learning Multi-agent Architectures
11 citations · 2019
📈 Most Prolific Year: 2019 (1 Papers)
🤝 Key Collaborators: 8
🏛 Institutions: Kabardino-Balkarian Scientific Center, Russian Academy of Sciences

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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