Machine learning often requires millions of examples to produce static, black-box models. In contrast, interactive task learning (ITL) emphasizes incremental knowledge acquisition from limited instruction provided by humans in modalities such as natural language. However, ITL systems often suffer from brittle, error-prone language parsing, which limits their usability. Large language models (LLMs) are resistant to brittleness but are not interpretable and cannot learn incrementally. We present VAL, an ITL system with a new philosophy for LLM/symbolic integration. By using LLMs only for specific tasks—such as predicate and argument selection—within an algorithmic framework, VAL reaps the benefits of LLMs to support interactive learning of hierarchical task knowledge from natural language. Acquired knowledge is human interpretable and generalizes to support execution of novel tasks without additional training. We studied users’ interactions with VAL in a video game setting, finding that most users could successfully teach VAL using language they felt was natural.

While recent advances in Artificial Intelligence and Machine Learning have demonstrated the potential for AI systems to outperform human experts in many domains, including games, AI systems still generally lack the ability to team with humans on complex tasks. One of the barriers to addressing this challenge is a lack of shared task domains in which to do basic research to study Human-Machine Teaming strategies. In our work, we employ speculative game design to create asymmetric cooperative games that can serve as test beds to study human-machine teaming challenges. In this paper, we will describe our general approach and detail the current state of our development efforts.