Autonomous agents are able to draw on a wide variety of potential sources of task knowledge; however current approaches invariably focus on only one or two. Here we investigate the challenges and impact of exploiting diverse knowledge sources to learn online, in one-shot, new tasks for a simulated office mobile robot. The resulting agent, developed in the Soar cognitive architecture, uses the following sources of domain and task knowledge: interaction with the environment, task execution and search knowledge, human natural language instruction, and responses retrieved from a large language model (GPT-3). We explore the distinct contributions of these knowledge sources and evaluate the performance of different combinations in terms of learning correct task knowledge and human workload. Results show that an agent's online integration of diverse knowledge sources improves one-shot task learning overall, reducing human feedback needed for rapid and reliable task learning.

翻译：自主智能体能够利用多种潜在的任务知识源，然而当前的方法通常只专注于其中一两种来源。本文针对模拟办公环境中移动机器人如何在线地、单次学习新任务，探究了利用多样化知识源所面临的挑战与影响。最终基于Soar认知架构构建的智能体使用了以下领域与任务知识源：与环境交互、任务执行与搜索知识、人类自然语言指令以及从大型语言模型（GPT-3）检索到的响应。我们探索了这些知识源的独特贡献，并从学习正确任务知识与人类工作量两方面评估了不同组合的性能。结果表明，智能体在线集成多种知识源能整体上提升单样本任务学习效果，减少了在快速且可靠地学习任务过程中所需的人类反馈。