Explaining Agent Behavior with Large Language Models

Autor:	Zhang, Xijia, Guo, Yue, Stepputtis, Simon, Sycara, Katia, Campbell, Joseph
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Intelligent agents such as robots are increasingly deployed in real-world, safety-critical settings. It is vital that these agents are able to explain the reasoning behind their decisions to human counterparts, however, their behavior is often produced by uninterpretable models such as deep neural networks. We propose an approach to generate natural language explanations for an agent's behavior based only on observations of states and actions, agnostic to the underlying model representation. We show how a compact representation of the agent's behavior can be learned and used to produce plausible explanations with minimal hallucination while affording user interaction with a pre-trained large language model. Through user studies and empirical experiments, we show that our approach generates explanations as helpful as those generated by a human domain expert while enabling beneficial interactions such as clarification and counterfactual queries. Comment: Human Multi-Robot Interaction Workshop at IROS 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.10346 Zobrazit plný text záznamu View this record from Arxiv