Topic #

Topic

1

Neuro-Symbolic Learning (1)

Li, Z., et al. (2023). Neuro-Symbolic Learning Yielding Logical Constraints. Proc. International Conference on Advances in Neural Information Processing Systems (NeurIPS). Curran Associates.  [Ref]

2

Neuro-Symbolic Learning (2)

Giunchiglia, E., et al. (2024). CCN+: A Neuro-Symbolic Framework for Deep Learning with Requirements. Approximate Reasoning, 171, Elsevier.  [Ref]

3

Neuro-Symbolic Learning (3)

Wang, C., et al. (2024). Imperative Learning: A Self-Supervised Neuro-Symbolic Learning Framework for Robot Autonomy. Robotics Research, Sage.  [Ref]

4

Neuro-Symbolic Learning (4)

Cunnington, D., et al. (2024). The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning. Proc. International Conference on Neural-Symbolic Learning and Reasoning. Springer.   [Ref]

5

Neuro-Symbolic Planning (1)

Danesh, M.H., et al.  (2023). LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty. Proc. of Machine Learning Research (PMLR) for Conference on Robot Learning, 205. [Ref]

6

Neuro-Symbolic Planning (2)

de la Rosa, T., et al. (2024). TRIP-PAL: Travel Planning with Guarantees by Combining Large Language Models and Automated Planners. arXiv preprint arXiv:2406.10196.  [Ref]

7

Neuro-Symbolic Planning (3)

Dai, Z., et al. (2024). Optimal Scene Graph Planning with Large Language Model Guidance. Proc. IEEE International Conference on Robotics and Automation (ICRA). IEEE.  [Ref]

8

Neuro-Symbolic Planning (4)

Tuisov, A., et al. (2025). LLM-Generated Heuristics for AI Planning: Do We Even Need Domain-Independence Anymore?. arXiv preprint arXiv:2501.18784.  [Ref]

9

Neuro-Symbolic Planning (5)

Lee, C., et al. (2025). VeriPlan: Integrating Formal Verification and LLMs into End-User Planning. Proc. ACM International Conference on Human Factors in Computing Systems (CHI). ACM. [Ref]

10

Neuro-Symbolic Planning (6)

Bai, D., et al. (2025). TwoStep: Multi-Agent Task Planning Using Classical Planners and Large Language Models. arXiv preprint arXiv:2403.17246v2. [Ref]