Integrating Explanations In Learning LTL Specifications From Demonstrations

Gupta Ashutosh, Komp John, Rajput Abhay Singh, Shankaranarayanan Krishna, Trivedi Ashutosh, Varshney Namrita. Arxiv 2024

[Paper]    
Efficiency And Optimization Interpretability And Explainability Reinforcement Learning Responsible AI

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.

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