论文标题
整合各种知识来源，以在线学习新任务
Integrating Diverse Knowledge Sources for Online One-shot Learning of Novel Tasks
论文作者
论文摘要
自主代理能够利用各种潜在的任务知识来源。但是，目前的方法总是只关注一个或两个。在这里，我们调查了利用多元化知识源以在线学习的挑战和影响，以模拟办公室移动机器人的新任务。在SOAR认知体系结构中开发的结果代理使用以下域和任务知识的来源：与环境，任务执行和搜索知识，人类自然语言指导以及从大语言模型（GPT-3）检索的响应的互动。我们探讨了这些知识来源的不同贡献，并在学习正确的任务知识和人为工作量方面评估了不同组合的性能。结果表明，代理商在线整合各种知识来源可改善一声任务的整体学习，从而减少了快速可靠的任务学习所需的人类反馈。
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.
