ReasonPlanner: Enhancing Autonomous Planning with Temporal Knowledge Graphs and LLMs
We released our preprint ReasonPlanner: Enhancing Autonomous Planning in Dynamic Environments with Temporal Knowledge Graphs and LLMs on arXiv.
ReasonPlanner is a novel generalist agent designed for reflective thinking, planning, and interactive reasoning. It leverages LLMs to plan hypothetical trajectories by building a World Model based on a Temporal Knowledge Graph. The agent interacts with the environment using a natural language actor-critic module, where the actor translates the imagined trajectory into a sequence of actionable steps, and the critic determines if replanning is necessary.
ReasonPlanner significantly outperforms previous state-of-the-art prompting-based methods on the ScienceWorld benchmark by more than 1.8x, while being more sample-efficient and interpretable. It relies solely on frozen weights, requiring no gradient updates, and can be deployed without specialized knowledge of machine learning.
Co-authors: Minh Pham Dinh, Munira Syed, Michael G. Yankoski