Integrating Belief-Desire-Intention agents with large language models for reliable human–robot interaction and explainable Artificial Intelligence

摘要

This paper presents the development of an innovative communication interface between humans and robots, designed for human-in-the-loop interaction with a user interface through natural language. The novelty of the presented approach lies in integration of a Belief-Desire-Intention agent, communicating directly to a robot and ensuring safety properties and verifiable decision making, with large language models that excel in language understanding and generation. We establish a framework that leverages the strengths of both paradigms by allowing users to formulate commands in natural language, using the ability of large language models to interpret and ground them into actionable goals, with the proven ability of Belief-Desire Intention agents to perform verifiable reasoning and goal management. In addition, we utilize the ability of this agent to represent and store its mind state, including its belief base, plan library, and history of selected events and actions, to allow large language models based explanations of the system behavior. Our findings demonstrate that this architecture provides several benefits in terms of performance and safety, without impacting efficiency or requiring extensive integration effort. This research contributes a novel perspective on the combination of large language models with symbolic approaches for explainable Artificial Intelligence and aims at inspiring new developments in human-centered Artificial Intelligence systems.