Persistent Pseudopod Splitting is an Effective Chemotaxis Strategy in Shallow Gradients

Information Science, Artificial Intelligence, Life Sciences, Cell Biology, Bioengineering, Immunology, Biophysics,
chemotaxis   pseudopods   reinforcement learning   mechanical intelligence   shallow gradients  

Academic Background Chemotaxis is a critical behavior in which cells or microorganisms move directionally along chemical gradients, playing vital roles in physiological processes such as immune responses, wound healing, and pathogen infections. However, how cells select optimal motility modes (e.g., pseudopod splitting or de novo formation) in comp...

Empathy Level Alignment via Reinforcement Learning for Empathetic Response Generation

Information Science, Computer Science, Artificial Intelligence,
empathetic response generation   reinforcement learning   empathy communication mechanism   reward function   dialogue systems  

Research on Empathetic Response Generation in AI Dialogue Systems Academic Background With the rapid development of artificial intelligence technology, open-domain dialogue systems have gradually become a research hotspot. These systems aim to engage in natural and fluent conversations with users, providing reasonable responses. However, despite si...

Reinforcement Learned Multiagent Cooperative Navigation in Hybrid Environment with Relational Graph Learning

Engineering, Mechanical Engineering, Electrical Engineering, Information Science, Computer Science, Automation, Artificial Intelligence,
multiagent system   reinforcement learning   relational graph learning   hybrid environment   cooperative navigation   decentralized execution   collision avoidance  

Multi-agent Cooperative Navigation in Hybrid Environments: A New Reinforcement Learning Approach Based on Relational Graph Learning Mobile robotics is witnessing a surge in applications, fueled by advancements in artificial intelligence, with navigation capabilities being one of the core focus areas of research. Traditional navigation methods often...

Adaptive Composite Fixed-Time RL-Optimized Control for Nonlinear Systems and Its Application to Intelligent Ship Autopilot

Engineering, Mechanical Engineering, Ocean Engineering, Transportation Engineering, Electrical Engineering, Information Science, Computer Science, Automation, Artificial Intelligence,
adaptive control   fixed-time control   reinforcement learning   nonlinear systems   neural networks   intelligent ships   optimization methods  

Nonlinear Fixed-Time Reinforcement Learning Optimized Control for Intelligent Ship Autopilots In recent years, intelligent autopilot technology has gradually become a research hotspot in the field of automation control. For complex nonlinear systems, the design of optimized control strategies, especially the achievement of system stability and perf...

Q-Cogni: An Integrated Causal Reinforcement Learning Framework

Information Science, Computer Science, Artificial Intelligence,
causal inference   reinforcement learning   vehicle routing problem   autonomous causal structure discovery   model-free learning   model-based learning   interpretability  

Research Insight Report: Q-Cogni—An Integrated Causal Reinforcement Learning Framework In recent years, the rapid advancement of artificial intelligence (AI) has propelled researchers to explore the development of more efficient and interpretable reinforcement learning (RL) systems. Due to its ability to mimic human decision-making, reinforcement l...