Integrating Explanations in Learning LTL Specifications from Demonstrations
This work addresses the challenge of reliably translating human explanations into formal specifications for safety-critical domains, though it appears incremental as it builds on existing LLM and optimization methods.
This paper tackles the problem of learning Linear Temporal Logic (LTL) specifications from demonstrations by integrating human explanations, using a tool called Janaka that combines Large Language Models (LLMs) and optimization-based methods to improve translation and learning effectiveness.
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.