  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.
