Model Based Planning

Model-based planning aims to create artificial agents capable of efficiently achieving complex, long-horizon goals by leveraging predictive models of their environment. Current research emphasizes improving the robustness of these models to unforeseen situations, often integrating reinforcement learning techniques to handle novelties and incorporating large language models or other neural networks for enhanced reasoning and planning capabilities. This field is crucial for advancing autonomous robotics, AI planning systems, and other applications requiring intelligent decision-making in dynamic and uncertain environments, with recent work focusing on improving efficiency and scalability of planning algorithms.