Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies (2309.16025v1)

Published 27 Sep 2023 in cs.LG, cs.AI, and cs.RO

Abstract: Current methods of imitation learning (IL), primarily based on deep neural networks, offer efficient means for obtaining driving policies from real-world data but suffer from significant limitations in interpretability and generalizability. These shortcomings are particularly concerning in safety-critical applications like autonomous driving. In this paper, we address these limitations by introducing Symbolic Imitation Learning (SIL), a groundbreaking method that employs Inductive Logic Programming (ILP) to learn driving policies which are transparent, explainable and generalisable from available datasets. Utilizing the real-world highD dataset, we subject our method to a rigorous comparative analysis against prevailing neural-network-based IL methods. Our results demonstrate that SIL not only enhances the interpretability of driving policies but also significantly improves their applicability across varied driving situations. Hence, this work offers a novel pathway to more reliable and safer autonomous driving systems, underscoring the potential of integrating ILP into the domain of IL.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (24)
  1. Inductive logic programming in answer set programming. In International conference on inductive logic programming, pp.  91–97. Springer, 2011.
  2. Inductive logic programming at 30: a new introduction. Journal of Artificial Intelligence Research, 74:765–850, 2022.
  3. Learning programs by learning from failures. Machine Learning, 110:801–856, 2021.
  4. Turning 30: New ideas in inductive logic programming. arXiv preprint arXiv:2002.11002, 2020.
  5. Explainable artificial intelligence: A survey. In 2018 41st International convention on information and communication technology, electronics and microelectronics (MIPRO), pp. 0210–0215. IEEE, 2018.
  6. Survey of imitation learning for robotic manipulation. International Journal of Intelligent Robotics and Applications, 3:362–369, 2019.
  7. A divergence minimization perspective on imitation learning methods. In Conference on Robot Learning, pp.  1259–1277. PMLR, 2020.
  8. Neuro-symbolic artificial intelligence: The state of the art. 2022.
  9. Interpretable learning for self-driving cars by visualizing causal attention. In Proceedings of the IEEE international conference on computer vision, pp.  2942–2950, 2017.
  10. Neuro-symbolic reinforcement learning with first-order logic. arXiv preprint arXiv:2110.10963, 2021.
  11. The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp.  2118–2125. IEEE, 2018.
  12. Vladimir Lifschitz. Answer set programming. Springer Heidelberg, 2019.
  13. Agile autonomous driving using end-to-end deep imitation learning. arXiv preprint arXiv:1709.07174, 2017.
  14. Imitation learning for agile autonomous driving. The International Journal of Robotics Research, 39(2-3):286–302, 2020.
  15. Neuro-symbolic artificial intelligence. AI Communications, 34(3):197–209, 2021.
  16. Towards safe autonomous driving policies using a neuro-symbolic deep reinforcement learning approach. arXiv preprint arXiv:2307.01316, 2023.
  17. An interpretable deep reinforcement learning approach to autonomous driving. In IJCAI Workshop on Artificial Intelligence for Automous Driving, 2022.
  18. Neuro-symbolic program search for autonomous driving decision module design. In Conference on Robot Learning, pp.  21–30. PMLR, 2021.
  19. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems, 32(11):4793–4813, 2020.
  20. Reinforcement learning with knowledge representation and reasoning: A brief survey. arXiv preprint arXiv:2304.12090, 2023.
  21. Query-efficient imitation learning for end-to-end autonomous driving. arXiv preprint arXiv:1605.06450, 2016.
  22. A survey on neural network interpretability. IEEE Transactions on Emerging Topics in Computational Intelligence, 5(5):726–742, 2021.
  23. Off-policy imitation learning from observations. Advances in Neural Information Processing Systems, 33:12402–12413, 2020.
  24. Differentiable logic machines. arXiv preprint arXiv:2102.11529, 2021.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now