Evaluating Diverse Knowledge Sources for Online One-shot Learning of Novel Tasks
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Online 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, in one-shot, new tasks for a simulated household 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 planning 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, human workload, and computational costs. The results from combining all sources demonstrate that integration improves one-shot task learning overall in terms of computational costs and human workload.
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