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Semantic Role Assisted Natural Language Rule Formalization for Intelligent Vehicle

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Rules and Reasoning (RuleML+RR 2023)

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This paper proposes a novel pipeline to translate natural language rules and instructions for intelligent vehicles into temporal logic. The pipeline uses semantic role labeling (SRL), soft rule-based selection restrictions, and large language models (LLMs) to extract predicates, arguments, and temporal aspects from natural language rules and instruction. We then use the language understanding capability of LLMs to generate temporal logic rules from unstructured natural language text and additional information provided by SRL. We envision our model as a human-in-the-loop system that can facilitate the automated rule formalization for planning and verification systems in automated driving and drone planning. We demonstrate that our method can generate semantically correct temporal logic formulas from natural language text and provide implicit explanations of the output by showing the intermediate reasoning steps involved. This paper illustrates the integration of additional semantic knowledge and LLM and its application for the intelligent system domain of automated driving and drone planning. Our generalizable pipeline can easily extend to new logic formalization types, traffic rules, drone planning instructions, and application domains.

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  1. 1.

  2. 2.

  3. 3.

  4. 4.

  5. 5.

  6. 6.

  7. 7.

  8. 8.


  1. Aréchiga, N.: Specifying safety of autonomous vehicles in signal temporal logic. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 58–63 (2019).

  2. Brown, T., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901 (2020)

    Google Scholar 

  3. Esterle, K., Aravantinos, V., Knoll, A.: From specifications to behavior: maneuver verification in a semantic state space. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 2140–2147 (2019).

  4. Fuggitti, F., Chakraborti, T.: NL2LTL - a python package for converting natural language (NL) instructions to linear temporal logic (LTL) formulas. In: AAAI (2023). system Demonstration

    Google Scholar 

  5. Gavran, I., Darulova, E., Majumdar, R.: Interactive synthesis of temporal specifications from examples and natural language. Proc. ACM Program. Lang. 4, 1–26 (2020).

    Article  Google Scholar 

  6. Gung, J., Palmer, M.: Predicate representations and polysemy in VerbNet semantic parsing. In: International Conference on Computational Semantics (2021)

    Google Scholar 

  7. Hahn, C., Schmitt, F., Tillman, J.J., Metzger, N., Siber, J., Finkbeiner, B.: Formal specifications from natural language. arXiv:abs/2206.01962 (2022)

  8. He, J., Bartocci, E., Ničković, D., Isakovic, H., Grosu, R.: DeepSTL - from English requirements to signal temporal logic. In: 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE), pp. 610–622 (2022).

  9. Hekmatnejad, M., et al.: Encoding and monitoring responsibility sensitive safety rules for automated vehicles in signal temporal logic. In: Proceedings of the 17th ACM-IEEE International Conference on Formal Methods and Models for System Design. Association for Computing Machinery (2019).

  10. Huang, W., Abbeel, P., Pathak, D., Mordatch, I.: Language models as zero-shot planners: extracting actionable knowledge for embodied agents. arXiv preprint: arXiv:2201.07207 (2022)

  11. Karimi, A., Duggirala, P.S.: Formalizing traffic rules for uncontrolled intersections. In: 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), pp. 41–50. IEEE (2020).

  12. Karlsson, J., Tumova, J.: Intention-aware motion planning with road rules. In: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), pp. 526–532 (2020).

  13. Konrad, S., Cheng, B.: Real-time specification patterns. In: Proceedings 27th International Conference on Software Engineering, 2005. ICSE 2005, pp. 372–381 (2005).

  14. Li, R., et al.: StarCoder: may the source be with you! arXiv:abs/2305.06161 (2023)

  15. Li, X., et al.: Differentiable logic layer for rule guided trajectory prediction. In: Conference on Robot Learning (2020)

    Google Scholar 

  16. Lin, J., et al.: Road traffic law adaptive decision-making for self-driving vehicles. In: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), pp. 2034–2041 (2022).

  17. Liu, J.X., et al.: Lang2LTL: translating natural language commands to temporal specification with large language models. In: CoRL Workshop on Language and Robot Learning (2022)

    Google Scholar 

  18. Liu, J., et al.: Generated knowledge prompting for commonsense reasoning. In: Annual Meeting of the Association for Computational Linguistics (2021)

    Google Scholar 

  19. Maierhofer, S., Moosbrugger, P., Althoff, M.: Formalization of intersection traffic rules in temporal logic. In: 2022 IEEE Intelligent Vehicles Symposium (IV), pp. 1135–1144 (2022).

  20. Maierhofer, S., Rettinger, A.K., Mayer, E.C., Althoff, M.: Formalization of interstate traffic rules in temporal logic. In: 2020 IEEE Intelligent Vehicles Symposium (IV), pp. 752–759 (2020).

  21. Muennighoff, N., et al.: Crosslingual generalization through multitask finetuning. arXiv:abs/2211.01786 (2022)

  22. Oh, Y., Patel, R., Nguyen, T., Huang, B., Pavlick, E., Tellex, S.: Planning with state abstractions for non-markovian task specifications. arXiv:abs/1905.12096 (2019)

  23. Palmer, M., Gildea, D., Kingsbury, P.: The proposition bank: an annotated corpus of semantic roles. Comput. Linguist. 31(1), 71–106 (2005).

    Article  Google Scholar 

  24. Palmer, M., Gildea, D., Xue, N.: Semantic Role Labeling. Synthesis Lectures on Human Language Technologies. Springer International Publishing, Cham (2010).

  25. Pan, J., Chou, G., Berenson, D.: Data-efficient learning of natural language to linear temporal logic translators for robot task specification. arXiv e-prints: arXiv:2303.08006 (2023)

  26. Ren, X., Yin, X., Li, S.: Synthesis of controllers for co-safe linear temporal logic specifications using reinforcement learning. In: 2021 40th Chinese Control Conference (CCC), pp. 2304–2309 (2021).

  27. Rizaldi, A., Immler, F., Althoff, M.: A formally verified checker of the safe distance traffic rules for autonomous vehicles. In: Rayadurgam, S., Tkachuk, O. (eds.) NFM 2016. LNCS, vol. 9690, pp. 175–190. Springer, Cham (2016).

    Chapter  Google Scholar 

  28. Rizaldi, A., et al.: Formalising and monitoring traffic rules for autonomous vehicles in Isabelle/HOL. In: Polikarpova, N., Schneider, S. (eds.) IFM 2017. LNCS, vol. 10510, pp. 50–66. Springer, Cham (2017).

    Chapter  Google Scholar 

  29. Schuler, K.K., Palmer, M.S.: VerbNet: a broad-coverage, comprehensive verb Lexicon. Ph.D. thesis, University of Pennsylvania, USA (2005)

    Google Scholar 

  30. Shi, P., Lin, J.: Simple BERT models for relation extraction and semantic role labeling. arXiv preprint: arXiv:1904.05255 (2019)

  31. Thati, P., Roşu, G.: Monitoring Algorithms for metric temporal logic specifications. Electr. Notes Theor. Comput. Sci. 113, 145–162 (2005).

    Article  Google Scholar 

  32. Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, pp. 6000–6010 (2017)

    Google Scholar 

  33. Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. In: Advances in Neural Information Processing Systems (2022)

    Google Scholar 

  34. Yao, S., et al.: ReAct: synergizing reasoning and acting in language models. In: International Conference on Learning Representations (ICLR) (2023)

    Google Scholar 

  35. Zhang, Q., Hong, D.K., Zhang, Z., Chen, Q.A., Mahlke, S., Mao, Z.M.: A systematic framework to identify violations of scenario-dependent driving rules in autonomous vehicle software. Proc. ACM Meas. Anal. Comput. Syst. 5(2), 1–25 (2021).

    Article  Google Scholar 

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This work is partially funded by German Federal Ministry for Economic Affairs and Climate Action within the “KI Wissen” project.

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Correspondence to Kumar Manas .

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Manas, K., Paschke, A. (2023). Semantic Role Assisted Natural Language Rule Formalization for Intelligent Vehicle. In: Fensel, A., Ozaki, A., Roman, D., Soylu, A. (eds) Rules and Reasoning. RuleML+RR 2023. Lecture Notes in Computer Science, vol 14244. Springer, Cham.

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