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Learning Automata-Based Task Knowledge Representation from Large-Scale Generative Language Models

2022-12-04 22:34:16
Yunhao Yang, Jean-Raphaël Gaglione, Ufuk Topcu
arXiv_CL
     
Abstract

Automata-based representations play an important role in control and planning in sequential decision-making, but obtaining high-level task knowledge for building automata is often difficult. Although large-scale generative language models (GLMs) can help automatically distill task knowledge, the textual outputs from GLMs are not directly utilizable in sequential decision-making. We resolve this problem by proposing a novel algorithm named GLM2FSA, which obtains high-level task knowledge, represented in a finite state automaton (FSA), from a given brief description of the task goal. GLM2FSA sends queries to a GLM for task knowledge in textual form and then builds a FSA to represent the textual knowledge. This algorithm fills the gap between text and automata-based representations, and the constructed FSA can be directly utilized in sequential decision-making. We provide examples to demonstrate how GLM2FSA constructs FSAs to represent knowledge encoded in the texts generated by the large-scale GLMs.

Abstract (translated)

URL

https://arxiv.org/abs/2212.01944

PDF

https://arxiv.org/pdf/2212.01944.pdf

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