Integrating Explanations in Learning LTL Specifications from Demonstrations
<|reference_start|>Integrating Explanations in Learning LTL Specifications from Demonstrations: This paper investigates whether recent advances in Large Language Models (LLMs) can assist in translating human explanations into a format that can robustly support learning Linear Temporal Logic (LTL) from demonstrations. Both LLMs and optimization-based methods can extract LTL specifications from demonstrations; however, they have distinct limitations. LLMs can quickly generate solutions and incorporate human explanations, but their lack of consistency and reliability hampers their applicability in safety-critical domains. On the other hand, optimization-based methods do provide formal guarantees but cannot process natural language explanations and face scalability challenges. We present a principled approach to combining LLMs and optimization-based methods to faithfully translate human explanations and demonstrations into LTL specifications. We have implemented a tool called Janaka based on our approach. Our experiments demonstrate the effectiveness of combining explanations with demonstrations in learning LTL specifications through several case studies.<|reference_end|>
@article{gupta2024integrating,
    title={Integrating Explanations in Learning LTL Specifications from
  Demonstrations},
    author={Ashutosh Gupta, John Komp, Abhay Singh Rajput, Krishna
  Shankaranarayanan, Ashutosh Trivedi, Namrita Varshney},
    journal={arXiv preprint arXiv:2404.02872},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02872},
    primaryClass={cs.AI}
}