Strategic Manipulation
Strategic manipulation encompasses the study of how agents, whether human or artificial, can influence systems or other agents to achieve desired outcomes. Current research focuses on developing methods to detect and mitigate manipulation in various contexts, including language models, robotic control, and multi-agent systems, often employing techniques like hierarchical planning, diffusion models, and transformer-based architectures. This field is crucial for building trustworthy AI systems and understanding human-computer interaction, with implications for improving the safety and robustness of robots and mitigating harmful biases in AI.
Papers
Novel Magnetic Actuation Strategies for Precise Ferrofluid Marble Manipulation in Magnetic Digital Microfluidics: Position Control and Applications
Mohammad Hossein Sarkhosh, Mohammad Hassan Dabirzadeh, Mohamad Ali Bijarchi, Hossein Nejat Pishkenari
Sharp-It: A Multi-view to Multi-view Diffusion Model for 3D Synthesis and Manipulation
Yiftach Edelstein, Or Patashnik, Dana Cohen-Bar, Lihi Zelnik-Manor