NSP: A Neuro-Symbolic Natural Language Navigational Planner
Published in ICMLA, 2024
NSP uses a feedback loop from the symbolic execution environment to the neural generation process to self-correct syntax errors and satisfy execution time constraints. We evaluate our neuro-symbolic approach using a benchmark suite with 1500 path-planning problems. The experimental evaluation shows that our neuro-symbolic approach produces 90.1% valid paths that are on average 19-77% shorter than state-of-the-art neural approaches.
Recommended citation: William English, Dominic Simon, Sumit Jha, and Rickard Ewetz, “NSP: A Neuro-Symbolic Natural Language Navigational Planner”, International Conference on Machine Learning and Applications (ICMLA), 2024. https://arxiv.org/abs/2409.06859