Action Model

Action models represent the effects of actions within a system, a crucial component for planning and decision-making in artificial intelligence. Current research focuses on learning these models from various data sources, including video, text, and plan traces, employing techniques like transformer networks, diffusion models, and algorithms designed for safety and efficiency in both single- and multi-agent settings. These advancements aim to improve the robustness and generalizability of AI agents across diverse tasks, impacting fields such as robotics, game playing, and human-robot interaction. The ability to accurately and efficiently learn action models is key to building more capable and adaptable autonomous systems.