VAL: Interactive Task Learning with GPT Dialog Parsing
Lane Lawley Christopher J. MacLellan
Reinforcement 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, in practice, ITL systems often
suffers from brittle, error-prone language parsing. 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.